├── Data
    └── Sunspots.csv
├── LICENSE
├── README.md
├── SampleCode
    ├── Readme.md
    ├── S+P_Week_1_Lesson_2.ipynb
    ├── S+P_Week_1_Lesson_3_Notebook.ipynb
    ├── S+P_Week_2_Exercise_Answer_ts_prediction_with_DNN.ipynb
    ├── S+P_Week_2_Lesson_1_ts_data_preparation_for_ML.ipynb
    ├── S+P_Week_2_Lesson_2_linear_regression_ts_data.ipynb
    ├── S+P_Week_2_Lesson_3_ts_prediction_with_DNN.ipynb
    ├── S+P_Week_3_Exercise_Answer_with_RNN_LSTM.ipynb
    ├── S+P_Week_3_Lesson_2_RNN_with_ts.ipynb
    ├── S+P_Week_3_Lesson_4_LSTM_with_ts.ipynb
    ├── S+P_Week_4_Exercise_Answer_temp_pred_using_DNN.ipynb
    ├── S+P_Week_4_Lesson_1.ipynb
    ├── S+P_Week_4_Lesson_3_Sunspot_prediction_using_DNN.ipynb
    └── Week_1_Exercise_Answer.ipynb
└── Time_Series_Forecasting_with_DNN.ipynb


/Data/Sunspots.csv:
--------------------------------------------------------------------------------
   1 | ,Date,Monthly Mean Total Sunspot Number
   2 | 0,1749-01-31,96.7
   3 | 1,1749-02-28,104.3
   4 | 2,1749-03-31,116.7
   5 | 3,1749-04-30,92.8
   6 | 4,1749-05-31,141.7
   7 | 5,1749-06-30,139.2
   8 | 6,1749-07-31,158.0
   9 | 7,1749-08-31,110.5
  10 | 8,1749-09-30,126.5
  11 | 9,1749-10-31,125.8
  12 | 10,1749-11-30,264.3
  13 | 11,1749-12-31,142.0
  14 | 12,1750-01-31,122.2
  15 | 13,1750-02-28,126.5
  16 | 14,1750-03-31,148.7
  17 | 15,1750-04-30,147.2
  18 | 16,1750-05-31,150.0
  19 | 17,1750-06-30,166.7
  20 | 18,1750-07-31,142.3
  21 | 19,1750-08-31,171.7
  22 | 20,1750-09-30,152.0
  23 | 21,1750-10-31,109.5
  24 | 22,1750-11-30,105.5
  25 | 23,1750-12-31,125.7
  26 | 24,1751-01-31,116.7
  27 | 25,1751-02-28,72.5
  28 | 26,1751-03-31,75.5
  29 | 27,1751-04-30,94.0
  30 | 28,1751-05-31,101.2
  31 | 29,1751-06-30,84.5
  32 | 30,1751-07-31,110.5
  33 | 31,1751-08-31,99.7
  34 | 32,1751-09-30,39.2
  35 | 33,1751-10-31,38.7
  36 | 34,1751-11-30,47.5
  37 | 35,1751-12-31,73.3
  38 | 36,1752-01-31,58.3
  39 | 37,1752-02-29,83.3
  40 | 38,1752-03-31,118.3
  41 | 39,1752-04-30,98.8
  42 | 40,1752-05-31,99.5
  43 | 41,1752-06-30,66.0
  44 | 42,1752-07-31,130.7
  45 | 43,1752-08-31,48.8
  46 | 44,1752-09-30,45.2
  47 | 45,1752-10-31,77.7
  48 | 46,1752-11-30,62.7
  49 | 47,1752-12-31,66.7
  50 | 48,1753-01-31,73.3
  51 | 49,1753-02-28,53.3
  52 | 50,1753-03-31,76.2
  53 | 51,1753-04-30,63.3
  54 | 52,1753-05-31,60.0
  55 | 53,1753-06-30,52.8
  56 | 54,1753-07-31,36.7
  57 | 55,1753-08-31,65.0
  58 | 56,1753-09-30,46.7
  59 | 57,1753-10-31,41.7
  60 | 58,1753-11-30,33.3
  61 | 59,1753-12-31,11.2
  62 | 60,1754-01-31,0.0
  63 | 61,1754-02-28,5.0
  64 | 62,1754-03-31,2.8
  65 | 63,1754-04-30,22.8
  66 | 64,1754-05-31,34.5
  67 | 65,1754-06-30,44.5
  68 | 66,1754-07-31,31.3
  69 | 67,1754-08-31,20.5
  70 | 68,1754-09-30,13.7
  71 | 69,1754-10-31,40.2
  72 | 70,1754-11-30,22.0
  73 | 71,1754-12-31,7.0
  74 | 72,1755-01-31,17.0
  75 | 73,1755-02-28,18.7
  76 | 74,1755-03-31,11.3
  77 | 75,1755-04-30,10.8
  78 | 76,1755-05-31,0.0
  79 | 77,1755-06-30,0.0
  80 | 78,1755-07-31,14.3
  81 | 79,1755-08-31,5.3
  82 | 80,1755-09-30,29.7
  83 | 81,1755-10-31,39.5
  84 | 82,1755-11-30,11.3
  85 | 83,1755-12-31,33.3
  86 | 84,1756-01-31,20.8
  87 | 85,1756-02-29,11.8
  88 | 86,1756-03-31,9.0
  89 | 87,1756-04-30,15.7
  90 | 88,1756-05-31,20.8
  91 | 89,1756-06-30,21.5
  92 | 90,1756-07-31,6.0
  93 | 91,1756-08-31,10.7
  94 | 92,1756-09-30,19.7
  95 | 93,1756-10-31,23.8
  96 | 94,1756-11-30,28.3
  97 | 95,1756-12-31,15.7
  98 | 96,1757-01-31,23.5
  99 | 97,1757-02-28,35.3
 100 | 98,1757-03-31,43.7
 101 | 99,1757-04-30,50.0
 102 | 100,1757-05-31,63.5
 103 | 101,1757-06-30,21.3
 104 | 102,1757-07-31,41.7
 105 | 103,1757-08-31,85.5
 106 | 104,1757-09-30,66.2
 107 | 105,1757-10-31,54.2
 108 | 106,1757-11-30,107.8
 109 | 107,1757-12-31,55.8
 110 | 108,1758-01-31,62.7
 111 | 109,1758-02-28,86.7
 112 | 110,1758-03-31,81.7
 113 | 111,1758-04-30,120.5
 114 | 112,1758-05-31,77.3
 115 | 113,1758-06-30,75.0
 116 | 114,1758-07-31,73.3
 117 | 115,1758-08-31,64.5
 118 | 116,1758-09-30,104.2
 119 | 117,1758-10-31,62.8
 120 | 118,1758-11-30,71.7
 121 | 119,1758-12-31,71.7
 122 | 120,1759-01-31,80.5
 123 | 121,1759-02-28,73.3
 124 | 122,1759-03-31,78.0
 125 | 123,1759-04-30,78.3
 126 | 124,1759-05-31,81.7
 127 | 125,1759-06-30,83.3
 128 | 126,1759-07-31,85.0
 129 | 127,1759-08-31,118.8
 130 | 128,1759-09-30,128.7
 131 | 129,1759-10-31,99.5
 132 | 130,1759-11-30,77.2
 133 | 131,1759-12-31,95.0
 134 | 132,1760-01-31,112.2
 135 | 133,1760-02-29,99.2
 136 | 134,1760-03-31,124.5
 137 | 135,1760-04-30,97.2
 138 | 136,1760-05-31,120.0
 139 | 137,1760-06-30,80.5
 140 | 138,1760-07-31,110.0
 141 | 139,1760-08-31,126.0
 142 | 140,1760-09-30,102.2
 143 | 141,1760-10-31,84.3
 144 | 142,1760-11-30,99.5
 145 | 143,1760-12-31,101.7
 146 | 144,1761-01-31,116.7
 147 | 145,1761-02-28,151.7
 148 | 146,1761-03-31,134.5
 149 | 147,1761-04-30,119.5
 150 | 148,1761-05-31,178.7
 151 | 149,1761-06-30,165.5
 152 | 150,1761-07-31,156.8
 153 | 151,1761-08-31,151.8
 154 | 152,1761-09-30,167.8
 155 | 153,1761-10-31,147.8
 156 | 154,1761-11-30,149.5
 157 | 155,1761-12-31,76.7
 158 | 156,1762-01-31,73.0
 159 | 157,1762-02-28,121.3
 160 | 158,1762-03-31,76.2
 161 | 159,1762-04-30,100.3
 162 | 160,1762-05-31,66.5
 163 | 161,1762-06-30,128.5
 164 | 162,1762-07-31,56.3
 165 | 163,1762-08-31,112.8
 166 | 164,1762-09-30,114.2
 167 | 165,1762-10-31,115.5
 168 | 166,1762-11-30,129.7
 169 | 167,1762-12-31,128.7
 170 | 168,1763-01-31,94.2
 171 | 169,1763-02-28,53.2
 172 | 170,1763-03-31,57.0
 173 | 171,1763-04-30,54.8
 174 | 172,1763-05-31,54.5
 175 | 173,1763-06-30,59.7
 176 | 174,1763-07-31,90.3
 177 | 175,1763-08-31,44.2
 178 | 176,1763-09-30,113.5
 179 | 177,1763-10-31,77.2
 180 | 178,1763-11-30,101.5
 181 | 179,1763-12-31,102.3
 182 | 180,1764-01-31,99.5
 183 | 181,1764-02-29,99.5
 184 | 182,1764-03-31,67.0
 185 | 183,1764-04-30,57.3
 186 | 184,1764-05-31,73.8
 187 | 185,1764-06-30,50.0
 188 | 186,1764-07-31,50.0
 189 | 187,1764-08-31,50.0
 190 | 188,1764-09-30,47.0
 191 | 189,1764-10-31,46.7
 192 | 190,1764-11-30,43.3
 193 | 191,1764-12-31,42.8
 194 | 192,1765-01-31,40.0
 195 | 193,1765-02-28,43.3
 196 | 194,1765-03-31,41.7
 197 | 195,1765-04-30,36.7
 198 | 196,1765-05-31,33.7
 199 | 197,1765-06-30,33.3
 200 | 198,1765-07-31,45.0
 201 | 199,1765-08-31,49.5
 202 | 200,1765-09-30,26.7
 203 | 201,1765-10-31,23.3
 204 | 202,1765-11-30,23.3
 205 | 203,1765-12-31,21.7
 206 | 204,1766-01-31,20.0
 207 | 205,1766-02-28,18.3
 208 | 206,1766-03-31,61.0
 209 | 207,1766-04-30,10.0
 210 | 208,1766-05-31,44.7
 211 | 209,1766-06-30,5.0
 212 | 210,1766-07-31,5.5
 213 | 211,1766-08-31,6.7
 214 | 212,1766-09-30,7.2
 215 | 213,1766-10-31,8.3
 216 | 214,1766-11-30,9.5
 217 | 215,1766-12-31,32.0
 218 | 216,1767-01-31,45.7
 219 | 217,1767-02-28,50.0
 220 | 218,1767-03-31,71.7
 221 | 219,1767-04-30,54.8
 222 | 220,1767-05-31,49.7
 223 | 221,1767-06-30,55.5
 224 | 222,1767-07-31,36.5
 225 | 223,1767-08-31,68.0
 226 | 224,1767-09-30,71.2
 227 | 225,1767-10-31,73.5
 228 | 226,1767-11-30,91.2
 229 | 227,1767-12-31,88.8
 230 | 228,1768-01-31,89.2
 231 | 229,1768-02-29,110.2
 232 | 230,1768-03-31,77.2
 233 | 231,1768-04-30,71.2
 234 | 232,1768-05-31,129.5
 235 | 233,1768-06-30,129.0
 236 | 234,1768-07-31,87.7
 237 | 235,1768-08-31,111.3
 238 | 236,1768-09-30,124.7
 239 | 237,1768-10-31,129.7
 240 | 238,1768-11-30,151.0
 241 | 239,1768-12-31,186.3
 242 | 240,1769-01-31,123.2
 243 | 241,1769-02-28,107.0
 244 | 242,1769-03-31,107.2
 245 | 243,1769-04-30,161.2
 246 | 244,1769-05-31,122.7
 247 | 245,1769-06-30,157.3
 248 | 246,1769-07-31,197.7
 249 | 247,1769-08-31,200.5
 250 | 248,1769-09-30,248.0
 251 | 249,1769-10-31,263.7
 252 | 250,1769-11-30,246.8
 253 | 251,1769-12-31,186.7
 254 | 252,1770-01-31,173.3
 255 | 253,1770-02-28,237.5
 256 | 254,1770-03-31,133.5
 257 | 255,1770-04-30,85.0
 258 | 256,1770-05-31,116.8
 259 | 257,1770-06-30,138.8
 260 | 258,1770-07-31,183.0
 261 | 259,1770-08-31,210.5
 262 | 260,1770-09-30,174.0
 263 | 261,1770-10-31,172.7
 264 | 262,1770-11-30,220.3
 265 | 263,1770-12-31,170.5
 266 | 264,1771-01-31,60.0
 267 | 265,1771-02-28,77.0
 268 | 266,1771-03-31,77.8
 269 | 267,1771-04-30,108.2
 270 | 268,1771-05-31,254.5
 271 | 269,1771-06-30,199.2
 272 | 270,1771-07-31,112.8
 273 | 271,1771-08-31,97.5
 274 | 272,1771-09-30,169.0
 275 | 273,1771-10-31,150.0
 276 | 274,1771-11-30,166.2
 277 | 275,1771-12-31,159.5
 278 | 276,1772-01-31,168.2
 279 | 277,1772-02-29,151.3
 280 | 278,1772-03-31,51.8
 281 | 279,1772-04-30,153.7
 282 | 280,1772-05-31,63.3
 283 | 281,1772-06-30,95.0
 284 | 282,1772-07-31,128.8
 285 | 283,1772-08-31,93.7
 286 | 284,1772-09-30,84.2
 287 | 285,1772-10-31,131.0
 288 | 286,1772-11-30,102.2
 289 | 287,1772-12-31,106.7
 290 | 288,1773-01-31,91.0
 291 | 289,1773-02-28,48.3
 292 | 290,1773-03-31,85.3
 293 | 291,1773-04-30,54.8
 294 | 292,1773-05-31,68.5
 295 | 293,1773-06-30,47.3
 296 | 294,1773-07-31,46.2
 297 | 295,1773-08-31,21.2
 298 | 296,1773-09-30,48.8
 299 | 297,1773-10-31,43.8
 300 | 298,1773-11-30,68.2
 301 | 299,1773-12-31,72.0
 302 | 300,1774-01-31,78.0
 303 | 301,1774-02-28,109.0
 304 | 302,1774-03-31,92.8
 305 | 303,1774-04-30,73.0
 306 | 304,1774-05-31,85.5
 307 | 305,1774-06-30,47.5
 308 | 306,1774-07-31,29.2
 309 | 307,1774-08-31,11.0
 310 | 308,1774-09-30,13.2
 311 | 309,1774-10-31,23.3
 312 | 310,1774-11-30,29.5
 313 | 311,1774-12-31,20.3
 314 | 312,1775-01-31,7.3
 315 | 313,1775-02-28,0.0
 316 | 314,1775-03-31,19.3
 317 | 315,1775-04-30,18.7
 318 | 316,1775-05-31,6.5
 319 | 317,1775-06-30,20.5
 320 | 318,1775-07-31,1.7
 321 | 319,1775-08-31,13.2
 322 | 320,1775-09-30,5.3
 323 | 321,1775-10-31,9.3
 324 | 322,1775-11-30,25.2
 325 | 323,1775-12-31,13.2
 326 | 324,1776-01-31,36.2
 327 | 325,1776-02-29,19.3
 328 | 326,1776-03-31,10.5
 329 | 327,1776-04-30,36.3
 330 | 328,1776-05-31,18.7
 331 | 329,1776-06-30,31.7
 332 | 330,1776-07-31,1.7
 333 | 331,1776-08-31,40.3
 334 | 332,1776-09-30,26.7
 335 | 333,1776-10-31,50.0
 336 | 334,1776-11-30,58.3
 337 | 335,1776-12-31,66.7
 338 | 336,1777-01-31,75.0
 339 | 337,1777-02-28,60.8
 340 | 338,1777-03-31,65.0
 341 | 339,1777-04-30,159.2
 342 | 340,1777-05-31,133.8
 343 | 341,1777-06-30,134.5
 344 | 342,1777-07-31,158.3
 345 | 343,1777-08-31,186.7
 346 | 344,1777-09-30,193.7
 347 | 345,1777-10-31,177.5
 348 | 346,1777-11-30,243.3
 349 | 347,1777-12-31,262.2
 350 | 348,1778-01-31,295.5
 351 | 349,1778-02-28,182.2
 352 | 350,1778-03-31,223.3
 353 | 351,1778-04-30,241.7
 354 | 352,1778-05-31,398.2
 355 | 353,1778-06-30,286.0
 356 | 354,1778-07-31,255.0
 357 | 355,1778-08-31,233.3
 358 | 356,1778-09-30,286.2
 359 | 357,1778-10-31,260.5
 360 | 358,1778-11-30,250.5
 361 | 359,1778-12-31,175.0
 362 | 360,1779-01-31,191.2
 363 | 361,1779-02-28,276.2
 364 | 362,1779-03-31,196.7
 365 | 363,1779-04-30,241.7
 366 | 364,1779-05-31,233.3
 367 | 365,1779-06-30,189.5
 368 | 366,1779-07-31,238.3
 369 | 367,1779-08-31,186.7
 370 | 368,1779-09-30,185.0
 371 | 369,1779-10-31,206.7
 372 | 370,1779-11-30,190.0
 373 | 371,1779-12-31,183.3
 374 | 372,1780-01-31,116.7
 375 | 373,1780-02-29,163.3
 376 | 374,1780-03-31,163.3
 377 | 375,1780-04-30,158.3
 378 | 376,1780-05-31,178.7
 379 | 377,1780-06-30,146.7
 380 | 378,1780-07-31,143.3
 381 | 379,1780-08-31,143.3
 382 | 380,1780-09-30,156.2
 383 | 381,1780-10-31,128.3
 384 | 382,1780-11-30,100.0
 385 | 383,1780-12-31,97.8
 386 | 384,1781-01-31,164.5
 387 | 385,1781-02-28,124.5
 388 | 386,1781-03-31,88.3
 389 | 387,1781-04-30,113.8
 390 | 388,1781-05-31,174.5
 391 | 389,1781-06-30,162.8
 392 | 390,1781-07-31,122.5
 393 | 391,1781-08-31,110.0
 394 | 392,1781-09-30,85.0
 395 | 393,1781-10-31,45.5
 396 | 394,1781-11-30,111.7
 397 | 395,1781-12-31,58.7
 398 | 396,1782-01-31,90.0
 399 | 397,1782-02-28,62.5
 400 | 398,1782-03-31,61.7
 401 | 399,1782-04-30,68.3
 402 | 400,1782-05-31,90.5
 403 | 401,1782-06-30,63.3
 404 | 402,1782-07-31,61.7
 405 | 403,1782-08-31,73.3
 406 | 404,1782-09-30,56.7
 407 | 405,1782-10-31,38.7
 408 | 406,1782-11-30,52.5
 409 | 407,1782-12-31,50.0
 410 | 408,1783-01-31,46.7
 411 | 409,1783-02-28,64.5
 412 | 410,1783-03-31,44.5
 413 | 411,1783-04-30,47.2
 414 | 412,1783-05-31,38.3
 415 | 413,1783-06-30,42.0
 416 | 414,1783-07-31,53.7
 417 | 415,1783-08-31,33.3
 418 | 416,1783-09-30,30.0
 419 | 417,1783-10-31,13.3
 420 | 418,1783-11-30,25.0
 421 | 419,1783-12-31,17.5
 422 | 420,1784-01-31,21.7
 423 | 421,1784-02-29,13.3
 424 | 422,1784-03-31,18.3
 425 | 423,1784-04-30,16.7
 426 | 424,1784-05-31,10.0
 427 | 425,1784-06-30,15.0
 428 | 426,1784-07-31,10.0
 429 | 427,1784-08-31,16.7
 430 | 428,1784-09-30,16.7
 431 | 429,1784-10-31,13.3
 432 | 430,1784-11-30,28.3
 433 | 431,1784-12-31,23.3
 434 | 432,1785-01-31,10.8
 435 | 433,1785-02-28,13.3
 436 | 434,1785-03-31,15.0
 437 | 435,1785-04-30,26.2
 438 | 436,1785-05-31,34.5
 439 | 437,1785-06-30,43.8
 440 | 438,1785-07-31,60.5
 441 | 439,1785-08-31,33.3
 442 | 440,1785-09-30,53.3
 443 | 441,1785-10-31,78.7
 444 | 442,1785-11-30,67.0
 445 | 443,1785-12-31,45.5
 446 | 444,1786-01-31,62.0
 447 | 445,1786-02-28,79.3
 448 | 446,1786-03-31,79.5
 449 | 447,1786-04-30,142.3
 450 | 448,1786-05-31,153.8
 451 | 449,1786-06-30,98.3
 452 | 450,1786-07-31,138.3
 453 | 451,1786-08-31,149.5
 454 | 452,1786-09-30,185.8
 455 | 453,1786-10-31,187.2
 456 | 454,1786-11-30,193.3
 457 | 455,1786-12-31,187.8
 458 | 456,1787-01-31,224.5
 459 | 457,1787-02-28,176.7
 460 | 458,1787-03-31,145.7
 461 | 459,1787-04-30,212.0
 462 | 460,1787-05-31,224.7
 463 | 461,1787-06-30,165.3
 464 | 462,1787-07-31,213.3
 465 | 463,1787-08-31,228.7
 466 | 464,1787-09-30,262.2
 467 | 465,1787-10-31,261.7
 468 | 466,1787-11-30,235.8
 469 | 467,1787-12-31,290.0
 470 | 468,1788-01-31,230.0
 471 | 469,1788-02-29,215.3
 472 | 470,1788-03-31,238.8
 473 | 471,1788-04-30,180.8
 474 | 472,1788-05-31,188.3
 475 | 473,1788-06-30,257.0
 476 | 474,1788-07-31,235.8
 477 | 475,1788-08-31,226.7
 478 | 476,1788-09-30,235.0
 479 | 477,1788-10-31,236.7
 480 | 478,1788-11-30,157.8
 481 | 479,1788-12-31,215.8
 482 | 480,1789-01-31,190.0
 483 | 481,1789-02-28,208.8
 484 | 482,1789-03-31,200.0
 485 | 483,1789-04-30,205.5
 486 | 484,1789-05-31,205.8
 487 | 485,1789-06-30,200.0
 488 | 486,1789-07-31,195.0
 489 | 487,1789-08-31,171.7
 490 | 488,1789-09-30,186.7
 491 | 489,1789-10-31,149.5
 492 | 490,1789-11-30,223.3
 493 | 491,1789-12-31,225.8
 494 | 492,1790-01-31,171.7
 495 | 493,1790-02-28,212.5
 496 | 494,1790-03-31,160.5
 497 | 495,1790-04-30,156.7
 498 | 496,1790-05-31,155.0
 499 | 497,1790-06-30,151.7
 500 | 498,1790-07-31,115.5
 501 | 499,1790-08-31,145.0
 502 | 500,1790-09-30,128.8
 503 | 501,1790-10-31,140.5
 504 | 502,1790-11-30,136.7
 505 | 503,1790-12-31,123.3
 506 | 504,1791-01-31,121.2
 507 | 505,1791-02-28,103.3
 508 | 506,1791-03-31,123.3
 509 | 507,1791-04-30,128.7
 510 | 508,1791-05-31,122.8
 511 | 509,1791-06-30,107.0
 512 | 510,1791-07-31,118.3
 513 | 511,1791-08-31,71.7
 514 | 512,1791-09-30,110.8
 515 | 513,1791-10-31,102.8
 516 | 514,1791-11-30,111.7
 517 | 515,1791-12-31,110.0
 518 | 516,1792-01-31,96.7
 519 | 517,1792-02-29,106.7
 520 | 518,1792-03-31,105.0
 521 | 519,1792-04-30,126.2
 522 | 520,1792-05-31,103.3
 523 | 521,1792-06-30,101.7
 524 | 522,1792-07-31,76.3
 525 | 523,1792-08-31,100.0
 526 | 524,1792-09-30,98.3
 527 | 525,1792-10-31,98.3
 528 | 526,1792-11-30,95.0
 529 | 527,1792-12-31,93.3
 530 | 528,1793-01-31,93.3
 531 | 529,1793-02-28,91.7
 532 | 530,1793-03-31,92.5
 533 | 531,1793-04-30,88.3
 534 | 532,1793-05-31,87.2
 535 | 533,1793-06-30,85.0
 536 | 534,1793-07-31,83.3
 537 | 535,1793-08-31,48.8
 538 | 536,1793-09-30,40.0
 539 | 537,1793-10-31,78.3
 540 | 538,1793-11-30,73.3
 541 | 539,1793-12-31,76.2
 542 | 540,1794-01-31,75.0
 543 | 541,1794-02-28,73.3
 544 | 542,1794-03-31,63.3
 545 | 543,1794-04-30,47.3
 546 | 544,1794-05-31,92.8
 547 | 545,1794-06-30,69.2
 548 | 546,1794-07-31,68.3
 549 | 547,1794-08-31,66.7
 550 | 548,1794-09-30,18.5
 551 | 549,1794-10-31,47.5
 552 | 550,1794-11-30,112.3
 553 | 551,1794-12-31,85.7
 554 | 552,1795-01-31,35.7
 555 | 553,1795-02-28,66.5
 556 | 554,1795-03-31,21.0
 557 | 555,1795-04-30,31.0
 558 | 556,1795-05-31,51.7
 559 | 557,1795-06-30,28.5
 560 | 558,1795-07-31,21.5
 561 | 559,1795-08-31,42.8
 562 | 560,1795-09-30,22.5
 563 | 561,1795-10-31,32.5
 564 | 562,1795-11-30,41.7
 565 | 563,1795-12-31,30.0
 566 | 564,1796-01-31,36.7
 567 | 565,1796-02-29,39.7
 568 | 566,1796-03-31,26.2
 569 | 567,1796-04-30,52.8
 570 | 568,1796-05-31,35.0
 571 | 569,1796-06-30,11.2
 572 | 570,1796-07-31,44.8
 573 | 571,1796-08-31,2.5
 574 | 572,1796-09-30,30.7
 575 | 573,1796-10-31,18.3
 576 | 574,1796-11-30,14.0
 577 | 575,1796-12-31,8.5
 578 | 576,1797-01-31,24.0
 579 | 577,1797-02-28,7.0
 580 | 578,1797-03-31,6.7
 581 | 579,1797-04-30,6.7
 582 | 580,1797-05-31,12.2
 583 | 581,1797-06-30,18.5
 584 | 582,1797-07-31,7.2
 585 | 583,1797-08-31,10.0
 586 | 584,1797-09-30,9.5
 587 | 585,1797-10-31,11.5
 588 | 586,1797-11-30,9.7
 589 | 587,1797-12-31,5.0
 590 | 588,1798-01-31,3.3
 591 | 589,1798-02-28,6.7
 592 | 590,1798-03-31,20.7
 593 | 591,1798-04-30,1.8
 594 | 592,1798-05-31,0.0
 595 | 593,1798-06-30,0.0
 596 | 594,1798-07-31,0.0
 597 | 595,1798-08-31,5.0
 598 | 596,1798-09-30,4.0
 599 | 597,1798-10-31,2.5
 600 | 598,1798-11-30,20.8
 601 | 599,1798-12-31,16.5
 602 | 600,1799-01-31,2.7
 603 | 601,1799-02-28,21.0
 604 | 602,1799-03-31,36.2
 605 | 603,1799-04-30,14.0
 606 | 604,1799-05-31,13.7
 607 | 605,1799-06-30,17.7
 608 | 606,1799-07-31,3.5
 609 | 607,1799-08-31,0.0
 610 | 608,1799-09-30,0.0
 611 | 609,1799-10-31,7.7
 612 | 610,1799-11-30,4.5
 613 | 611,1799-12-31,14.3
 614 | 612,1800-01-31,11.5
 615 | 613,1800-02-28,15.5
 616 | 614,1800-03-31,23.2
 617 | 615,1800-04-30,0.0
 618 | 616,1800-05-31,8.3
 619 | 617,1800-06-30,39.5
 620 | 618,1800-07-31,35.0
 621 | 619,1800-08-31,32.5
 622 | 620,1800-09-30,19.2
 623 | 621,1800-10-31,20.5
 624 | 622,1800-11-30,17.5
 625 | 623,1800-12-31,66.8
 626 | 624,1801-01-31,45.0
 627 | 625,1801-02-28,48.3
 628 | 626,1801-03-31,50.0
 629 | 627,1801-04-30,51.7
 630 | 628,1801-05-31,53.3
 631 | 629,1801-06-30,52.0
 632 | 630,1801-07-31,58.3
 633 | 631,1801-08-31,64.5
 634 | 632,1801-09-30,55.8
 635 | 633,1801-10-31,54.3
 636 | 634,1801-11-30,66.3
 637 | 635,1801-12-31,80.3
 638 | 636,1802-01-31,79.7
 639 | 637,1802-02-28,78.3
 640 | 638,1802-03-31,68.0
 641 | 639,1802-04-30,70.0
 642 | 640,1802-05-31,73.3
 643 | 641,1802-06-30,76.7
 644 | 642,1802-07-31,80.0
 645 | 643,1802-08-31,83.3
 646 | 644,1802-09-30,86.3
 647 | 645,1802-10-31,64.2
 648 | 646,1802-11-30,57.5
 649 | 647,1802-12-31,83.3
 650 | 648,1803-01-31,83.3
 651 | 649,1803-02-28,84.7
 652 | 650,1803-03-31,49.2
 653 | 651,1803-04-30,41.7
 654 | 652,1803-05-31,73.8
 655 | 653,1803-06-30,60.0
 656 | 654,1803-07-31,80.5
 657 | 655,1803-08-31,56.8
 658 | 656,1803-09-30,75.5
 659 | 657,1803-10-31,90.5
 660 | 658,1803-11-30,85.0
 661 | 659,1803-12-31,80.0
 662 | 660,1804-01-31,75.5
 663 | 661,1804-02-29,80.5
 664 | 662,1804-03-31,80.0
 665 | 663,1804-04-30,84.3
 666 | 664,1804-05-31,55.7
 667 | 665,1804-06-30,58.0
 668 | 666,1804-07-31,49.7
 669 | 667,1804-08-31,71.8
 670 | 668,1804-09-30,88.3
 671 | 669,1804-10-31,103.8
 672 | 670,1804-11-30,101.7
 673 | 671,1804-12-31,100.0
 674 | 672,1805-01-31,101.7
 675 | 673,1805-02-28,73.5
 676 | 674,1805-03-31,85.7
 677 | 675,1805-04-30,62.5
 678 | 676,1805-05-31,65.0
 679 | 677,1805-06-30,67.5
 680 | 678,1805-07-31,62.7
 681 | 679,1805-08-31,71.2
 682 | 680,1805-09-30,74.0
 683 | 681,1805-10-31,49.0
 684 | 682,1805-11-30,68.3
 685 | 683,1805-12-31,63.8
 686 | 684,1806-01-31,65.0
 687 | 685,1806-02-28,49.3
 688 | 686,1806-03-31,54.5
 689 | 687,1806-04-30,46.2
 690 | 688,1806-05-31,44.0
 691 | 689,1806-06-30,42.7
 692 | 690,1806-07-31,50.0
 693 | 691,1806-08-31,43.8
 694 | 692,1806-09-30,40.0
 695 | 693,1806-10-31,45.0
 696 | 694,1806-11-30,41.7
 697 | 695,1806-12-31,40.0
 698 | 696,1807-01-31,20.0
 699 | 697,1807-02-28,20.3
 700 | 698,1807-03-31,16.0
 701 | 699,1807-04-30,39.7
 702 | 700,1807-05-31,16.7
 703 | 701,1807-06-30,20.0
 704 | 702,1807-07-31,21.2
 705 | 703,1807-08-31,20.0
 706 | 704,1807-09-30,9.5
 707 | 705,1807-10-31,13.3
 708 | 706,1807-11-30,4.3
 709 | 707,1807-12-31,0.0
 710 | 708,1808-01-31,0.0
 711 | 709,1808-02-29,7.5
 712 | 710,1808-03-31,0.0
 713 | 711,1808-04-30,20.5
 714 | 712,1808-05-31,22.5
 715 | 713,1808-06-30,22.5
 716 | 714,1808-07-31,11.2
 717 | 715,1808-08-31,13.3
 718 | 716,1808-09-30,19.5
 719 | 717,1808-10-31,7.8
 720 | 718,1808-11-30,17.5
 721 | 719,1808-12-31,20.5
 722 | 720,1809-01-31,12.0
 723 | 721,1809-02-28,15.3
 724 | 722,1809-03-31,1.5
 725 | 723,1809-04-30,4.2
 726 | 724,1809-05-31,3.3
 727 | 725,1809-06-30,12.8
 728 | 726,1809-07-31,0.5
 729 | 727,1809-08-31,0.3
 730 | 728,1809-09-30,0.7
 731 | 729,1809-10-31,0.0
 732 | 730,1809-11-30,0.0
 733 | 731,1809-12-31,0.0
 734 | 732,1810-01-31,0.0
 735 | 733,1810-02-28,0.0
 736 | 734,1810-03-31,0.0
 737 | 735,1810-04-30,0.0
 738 | 736,1810-05-31,0.0
 739 | 737,1810-06-30,0.0
 740 | 738,1810-07-31,0.0
 741 | 739,1810-08-31,0.0
 742 | 740,1810-09-30,0.0
 743 | 741,1810-10-31,0.0
 744 | 742,1810-11-30,0.0
 745 | 743,1810-12-31,0.0
 746 | 744,1811-01-31,0.0
 747 | 745,1811-02-28,0.0
 748 | 746,1811-03-31,0.0
 749 | 747,1811-04-30,0.0
 750 | 748,1811-05-31,0.0
 751 | 749,1811-06-30,0.0
 752 | 750,1811-07-31,11.0
 753 | 751,1811-08-31,0.0
 754 | 752,1811-09-30,4.0
 755 | 753,1811-10-31,10.2
 756 | 754,1811-11-30,1.3
 757 | 755,1811-12-31,1.8
 758 | 756,1812-01-31,18.8
 759 | 757,1812-02-29,3.2
 760 | 758,1812-03-31,1.2
 761 | 759,1812-04-30,0.0
 762 | 760,1812-05-31,1.7
 763 | 761,1812-06-30,2.2
 764 | 762,1812-07-31,0.8
 765 | 763,1812-08-31,26.0
 766 | 764,1812-09-30,8.7
 767 | 765,1812-10-31,6.5
 768 | 766,1812-11-30,13.2
 769 | 767,1812-12-31,16.8
 770 | 768,1813-01-31,0.0
 771 | 769,1813-02-28,17.2
 772 | 770,1813-03-31,3.2
 773 | 771,1813-04-30,27.7
 774 | 772,1813-05-31,9.2
 775 | 773,1813-06-30,18.7
 776 | 774,1813-07-31,30.5
 777 | 775,1813-08-31,14.0
 778 | 776,1813-09-30,25.5
 779 | 777,1813-10-31,46.3
 780 | 778,1813-11-30,27.8
 781 | 779,1813-12-31,23.8
 782 | 780,1814-01-31,37.0
 783 | 781,1814-02-28,20.0
 784 | 782,1814-03-31,9.5
 785 | 783,1814-04-30,39.7
 786 | 784,1814-05-31,9.7
 787 | 785,1814-06-30,24.8
 788 | 786,1814-07-31,30.8
 789 | 787,1814-08-31,3.8
 790 | 788,1814-09-30,13.5
 791 | 789,1814-10-31,32.2
 792 | 790,1814-11-30,24.2
 793 | 791,1814-12-31,33.5
 794 | 792,1815-01-31,32.0
 795 | 793,1815-02-28,53.7
 796 | 794,1815-03-31,43.7
 797 | 795,1815-04-30,52.7
 798 | 796,1815-05-31,16.3
 799 | 797,1815-06-30,93.2
 800 | 798,1815-07-31,59.2
 801 | 799,1815-08-31,78.7
 802 | 800,1815-09-30,52.5
 803 | 801,1815-10-31,55.8
 804 | 802,1815-11-30,62.0
 805 | 803,1815-12-31,108.3
 806 | 804,1816-01-31,43.8
 807 | 805,1816-02-29,114.7
 808 | 806,1816-03-31,122.8
 809 | 807,1816-04-30,98.0
 810 | 808,1816-05-31,73.8
 811 | 809,1816-06-30,72.7
 812 | 810,1816-07-31,64.7
 813 | 811,1816-08-31,38.7
 814 | 812,1816-09-30,79.7
 815 | 813,1816-10-31,94.0
 816 | 814,1816-11-30,63.5
 817 | 815,1816-12-31,49.8
 818 | 816,1817-01-31,60.7
 819 | 817,1817-02-28,96.5
 820 | 818,1817-03-31,160.3
 821 | 819,1817-04-30,44.0
 822 | 820,1817-05-31,35.3
 823 | 821,1817-06-30,66.7
 824 | 822,1817-07-31,83.3
 825 | 823,1817-08-31,75.0
 826 | 824,1817-09-30,61.2
 827 | 825,1817-10-31,42.7
 828 | 826,1817-11-30,48.2
 829 | 827,1817-12-31,47.3
 830 | 828,1818-01-31,58.1
 831 | 829,1818-02-28,37.4
 832 | 830,1818-03-31,42.4
 833 | 831,1818-04-30,57.5
 834 | 832,1818-05-31,88.5
 835 | 833,1818-06-30,60.8
 836 | 834,1818-07-31,46.8
 837 | 835,1818-08-31,52.6
 838 | 836,1818-09-30,43.4
 839 | 837,1818-10-31,52.8
 840 | 838,1818-11-30,18.3
 841 | 839,1818-12-31,43.0
 842 | 840,1819-01-31,54.7
 843 | 841,1819-02-28,34.6
 844 | 842,1819-03-31,6.3
 845 | 843,1819-04-30,33.7
 846 | 844,1819-05-31,32.6
 847 | 845,1819-06-30,58.3
 848 | 846,1819-07-31,52.5
 849 | 847,1819-08-31,43.5
 850 | 848,1819-09-30,24.8
 851 | 849,1819-10-31,45.9
 852 | 850,1819-11-30,41.8
 853 | 851,1819-12-31,50.9
 854 | 852,1820-01-31,32.0
 855 | 853,1820-02-29,44.4
 856 | 854,1820-03-31,7.5
 857 | 855,1820-04-30,32.3
 858 | 856,1820-05-31,48.9
 859 | 857,1820-06-30,17.9
 860 | 858,1820-07-31,34.1
 861 | 859,1820-08-31,43.1
 862 | 860,1820-09-30,8.6
 863 | 861,1820-10-31,14.9
 864 | 862,1820-11-30,13.2
 865 | 863,1820-12-31,15.1
 866 | 864,1821-01-31,36.0
 867 | 865,1821-02-28,7.0
 868 | 866,1821-03-31,9.5
 869 | 867,1821-04-30,15.2
 870 | 868,1821-05-31,2.8
 871 | 869,1821-06-30,3.0
 872 | 870,1821-07-31,4.3
 873 | 871,1821-08-31,8.0
 874 | 872,1821-09-30,7.3
 875 | 873,1821-10-31,31.4
 876 | 874,1821-11-30,7.3
 877 | 875,1821-12-31,0.4
 878 | 876,1822-01-31,0.0
 879 | 877,1822-02-28,1.5
 880 | 878,1822-03-31,26.9
 881 | 879,1822-04-30,21.7
 882 | 880,1822-05-31,2.4
 883 | 881,1822-06-30,9.3
 884 | 882,1822-07-31,13.2
 885 | 883,1822-08-31,3.4
 886 | 884,1822-09-30,0.0
 887 | 885,1822-10-31,0.6
 888 | 886,1822-11-30,0.0
 889 | 887,1822-12-31,0.0
 890 | 888,1823-01-31,0.0
 891 | 889,1823-02-28,0.0
 892 | 890,1823-03-31,1.0
 893 | 891,1823-04-30,0.0
 894 | 892,1823-05-31,0.0
 895 | 893,1823-06-30,0.0
 896 | 894,1823-07-31,0.8
 897 | 895,1823-08-31,0.0
 898 | 896,1823-09-30,0.0
 899 | 897,1823-10-31,0.0
 900 | 898,1823-11-30,0.0
 901 | 899,1823-12-31,34.0
 902 | 900,1824-01-31,36.0
 903 | 901,1824-02-29,18.0
 904 | 902,1824-03-31,0.0
 905 | 903,1824-04-30,32.3
 906 | 904,1824-05-31,4.6
 907 | 905,1824-06-30,0.0
 908 | 906,1824-07-31,0.0
 909 | 907,1824-08-31,2.3
 910 | 908,1824-09-30,34.3
 911 | 909,1824-10-31,42.0
 912 | 910,1824-11-30,0.0
 913 | 911,1824-12-31,1.4
 914 | 912,1825-01-31,8.4
 915 | 913,1825-02-28,25.9
 916 | 914,1825-03-31,37.4
 917 | 915,1825-04-30,6.2
 918 | 916,1825-05-31,25.8
 919 | 917,1825-06-30,25.6
 920 | 918,1825-07-31,51.5
 921 | 919,1825-08-31,42.8
 922 | 920,1825-09-30,26.2
 923 | 921,1825-10-31,26.0
 924 | 922,1825-11-30,19.6
 925 | 923,1825-12-31,36.7
 926 | 924,1826-01-31,29.4
 927 | 925,1826-02-28,30.2
 928 | 926,1826-03-31,61.3
 929 | 927,1826-04-30,40.0
 930 | 928,1826-05-31,53.9
 931 | 929,1826-06-30,61.7
 932 | 930,1826-07-31,87.5
 933 | 931,1826-08-31,66.0
 934 | 932,1826-09-30,31.6
 935 | 933,1826-10-31,84.4
 936 | 934,1826-11-30,65.8
 937 | 935,1826-12-31,113.6
 938 | 936,1827-01-31,57.7
 939 | 937,1827-02-28,79.0
 940 | 938,1827-03-31,96.3
 941 | 939,1827-04-30,76.7
 942 | 940,1827-05-31,93.8
 943 | 941,1827-06-30,94.4
 944 | 942,1827-07-31,70.4
 945 | 943,1827-08-31,89.4
 946 | 944,1827-09-30,82.6
 947 | 945,1827-10-31,93.5
 948 | 946,1827-11-30,80.4
 949 | 947,1827-12-31,76.8
 950 | 948,1828-01-31,88.0
 951 | 949,1828-02-29,107.3
 952 | 950,1828-03-31,108.5
 953 | 951,1828-04-30,101.9
 954 | 952,1828-05-31,148.6
 955 | 953,1828-06-30,163.4
 956 | 954,1828-07-31,90.4
 957 | 955,1828-08-31,127.3
 958 | 956,1828-09-30,83.9
 959 | 957,1828-10-31,91.1
 960 | 958,1828-11-30,95.0
 961 | 959,1828-12-31,78.1
 962 | 960,1829-01-31,71.6
 963 | 961,1829-02-28,82.3
 964 | 962,1829-03-31,120.6
 965 | 963,1829-04-30,158.4
 966 | 964,1829-05-31,112.3
 967 | 965,1829-06-30,123.2
 968 | 966,1829-07-31,151.5
 969 | 967,1829-08-31,129.3
 970 | 968,1829-09-30,88.0
 971 | 969,1829-10-31,95.3
 972 | 970,1829-11-30,112.7
 973 | 971,1829-12-31,94.1
 974 | 972,1830-01-31,87.0
 975 | 973,1830-02-28,120.1
 976 | 974,1830-03-31,141.0
 977 | 975,1830-04-30,177.3
 978 | 976,1830-05-31,110.5
 979 | 977,1830-06-30,108.4
 980 | 978,1830-07-31,73.2
 981 | 979,1830-08-31,84.6
 982 | 980,1830-09-30,103.4
 983 | 981,1830-10-31,140.6
 984 | 982,1830-11-30,135.4
 985 | 983,1830-12-31,136.8
 986 | 984,1831-01-31,79.0
 987 | 985,1831-02-28,83.6
 988 | 986,1831-03-31,155.8
 989 | 987,1831-04-30,90.9
 990 | 988,1831-05-31,63.4
 991 | 989,1831-06-30,55.6
 992 | 990,1831-07-31,75.4
 993 | 991,1831-08-31,91.6
 994 | 992,1831-09-30,63.2
 995 | 993,1831-10-31,77.1
 996 | 994,1831-11-30,72.5
 997 | 995,1831-12-31,48.3
 998 | 996,1832-01-31,51.5
 999 | 997,1832-02-29,92.6
1000 | 998,1832-03-31,91.9
1001 | 999,1832-04-30,44.8
1002 | 1000,1832-05-31,68.9
1003 | 1001,1832-06-30,44.5
1004 | 1002,1832-07-31,23.3
1005 | 1003,1832-08-31,14.8
1006 | 1004,1832-09-30,13.7
1007 | 1005,1832-10-31,35.2
1008 | 1006,1832-11-30,23.8
1009 | 1007,1832-12-31,45.8
1010 | 1008,1833-01-31,18.7
1011 | 1009,1833-02-28,24.9
1012 | 1010,1833-03-31,19.6
1013 | 1011,1833-04-30,4.6
1014 | 1012,1833-05-31,21.5
1015 | 1013,1833-06-30,1.7
1016 | 1014,1833-07-31,11.6
1017 | 1015,1833-08-31,9.5
1018 | 1016,1833-09-30,19.3
1019 | 1017,1833-10-31,12.5
1020 | 1018,1833-11-30,9.8
1021 | 1019,1833-12-31,16.6
1022 | 1020,1834-01-31,8.2
1023 | 1021,1834-02-28,30.1
1024 | 1022,1834-03-31,6.5
1025 | 1023,1834-04-30,2.4
1026 | 1024,1834-05-31,14.8
1027 | 1025,1834-06-30,13.1
1028 | 1026,1834-07-31,14.4
1029 | 1027,1834-08-31,6.6
1030 | 1028,1834-09-30,19.2
1031 | 1029,1834-10-31,41.3
1032 | 1030,1834-11-30,50.9
1033 | 1031,1834-12-31,57.5
1034 | 1032,1835-01-31,12.4
1035 | 1033,1835-02-28,40.7
1036 | 1034,1835-03-31,32.7
1037 | 1035,1835-04-30,102.4
1038 | 1036,1835-05-31,72.6
1039 | 1037,1835-06-30,55.2
1040 | 1038,1835-07-31,99.6
1041 | 1039,1835-08-31,98.5
1042 | 1040,1835-09-30,168.0
1043 | 1041,1835-10-31,158.6
1044 | 1042,1835-11-30,166.7
1045 | 1043,1835-12-31,129.3
1046 | 1044,1836-01-31,147.6
1047 | 1045,1836-02-29,179.4
1048 | 1046,1836-03-31,163.7
1049 | 1047,1836-04-30,238.0
1050 | 1048,1836-05-31,185.7
1051 | 1049,1836-06-30,207.9
1052 | 1050,1836-07-31,194.6
1053 | 1051,1836-08-31,179.6
1054 | 1052,1836-09-30,158.5
1055 | 1053,1836-10-31,228.9
1056 | 1054,1836-11-30,201.5
1057 | 1055,1836-12-31,343.8
1058 | 1056,1837-01-31,313.4
1059 | 1057,1837-02-28,292.6
1060 | 1058,1837-03-31,224.3
1061 | 1059,1837-04-30,230.3
1062 | 1060,1837-05-31,186.1
1063 | 1061,1837-06-30,263.4
1064 | 1062,1837-07-31,271.2
1065 | 1063,1837-08-31,223.5
1066 | 1064,1837-09-30,160.4
1067 | 1065,1837-10-31,206.2
1068 | 1066,1837-11-30,178.4
1069 | 1067,1837-12-31,216.1
1070 | 1068,1838-01-31,241.5
1071 | 1069,1838-02-28,141.3
1072 | 1070,1838-03-31,234.5
1073 | 1071,1838-04-30,211.1
1074 | 1072,1838-05-31,229.4
1075 | 1073,1838-06-30,157.6
1076 | 1074,1838-07-31,180.4
1077 | 1075,1838-08-31,131.3
1078 | 1076,1838-09-30,122.7
1079 | 1077,1838-10-31,151.5
1080 | 1078,1838-11-30,129.2
1081 | 1079,1838-12-31,132.9
1082 | 1080,1839-01-31,175.9
1083 | 1081,1839-02-28,170.8
1084 | 1082,1839-03-31,129.5
1085 | 1083,1839-04-30,102.9
1086 | 1084,1839-05-31,89.6
1087 | 1085,1839-06-30,91.2
1088 | 1086,1839-07-31,141.3
1089 | 1087,1839-08-31,218.8
1090 | 1088,1839-09-30,221.1
1091 | 1089,1839-10-31,151.5
1092 | 1090,1839-11-30,114.7
1093 | 1091,1839-12-31,106.2
1094 | 1092,1840-01-31,135.4
1095 | 1093,1840-02-29,146.1
1096 | 1094,1840-03-31,112.9
1097 | 1095,1840-04-30,109.8
1098 | 1096,1840-05-31,115.4
1099 | 1097,1840-06-30,80.9
1100 | 1098,1840-07-31,101.0
1101 | 1099,1840-08-31,96.4
1102 | 1100,1840-09-30,123.3
1103 | 1101,1840-10-31,91.7
1104 | 1102,1840-11-30,90.4
1105 | 1103,1840-12-31,89.5
1106 | 1104,1841-01-31,40.1
1107 | 1105,1841-02-28,49.9
1108 | 1106,1841-03-31,49.6
1109 | 1107,1841-04-30,66.9
1110 | 1108,1841-05-31,112.4
1111 | 1109,1841-06-30,92.8
1112 | 1110,1841-07-31,51.4
1113 | 1111,1841-08-31,65.4
1114 | 1112,1841-09-30,60.9
1115 | 1113,1841-10-31,47.4
1116 | 1114,1841-11-30,32.9
1117 | 1115,1841-12-31,64.8
1118 | 1116,1842-01-31,34.0
1119 | 1117,1842-02-28,36.8
1120 | 1118,1842-03-31,36.2
1121 | 1119,1842-04-30,44.9
1122 | 1120,1842-05-31,41.4
1123 | 1121,1842-06-30,34.2
1124 | 1122,1842-07-31,21.0
1125 | 1123,1842-08-31,44.4
1126 | 1124,1842-09-30,30.6
1127 | 1125,1842-10-31,63.5
1128 | 1126,1842-11-30,67.5
1129 | 1127,1842-12-31,29.3
1130 | 1128,1843-01-31,22.2
1131 | 1129,1843-02-28,5.9
1132 | 1130,1843-03-31,13.9
1133 | 1131,1843-04-30,15.8
1134 | 1132,1843-05-31,35.1
1135 | 1133,1843-06-30,17.6
1136 | 1134,1843-07-31,15.9
1137 | 1135,1843-08-31,19.6
1138 | 1136,1843-09-30,6.9
1139 | 1137,1843-10-31,8.9
1140 | 1138,1843-11-30,31.7
1141 | 1139,1843-12-31,21.2
1142 | 1140,1844-01-31,15.7
1143 | 1141,1844-02-29,24.5
1144 | 1142,1844-03-31,22.5
1145 | 1143,1844-04-30,34.6
1146 | 1144,1844-05-31,19.2
1147 | 1145,1844-06-30,6.2
1148 | 1146,1844-07-31,35.2
1149 | 1147,1844-08-31,39.7
1150 | 1148,1844-09-30,11.6
1151 | 1149,1844-10-31,35.9
1152 | 1150,1844-11-30,17.9
1153 | 1151,1844-12-31,36.0
1154 | 1152,1845-01-31,42.8
1155 | 1153,1845-02-28,72.8
1156 | 1154,1845-03-31,72.1
1157 | 1155,1845-04-30,95.0
1158 | 1156,1845-05-31,79.7
1159 | 1157,1845-06-30,51.8
1160 | 1158,1845-07-31,51.0
1161 | 1159,1845-08-31,53.9
1162 | 1160,1845-09-30,49.3
1163 | 1161,1845-10-31,67.8
1164 | 1162,1845-11-30,65.7
1165 | 1163,1845-12-31,99.5
1166 | 1164,1846-01-31,64.6
1167 | 1165,1846-02-28,84.9
1168 | 1166,1846-03-31,106.5
1169 | 1167,1846-04-30,115.5
1170 | 1168,1846-05-31,99.9
1171 | 1169,1846-06-30,108.4
1172 | 1170,1846-07-31,77.5
1173 | 1171,1846-08-31,91.3
1174 | 1172,1846-09-30,178.6
1175 | 1173,1846-10-31,93.4
1176 | 1174,1846-11-30,100.6
1177 | 1175,1846-12-31,109.1
1178 | 1176,1847-01-31,104.4
1179 | 1177,1847-02-28,74.9
1180 | 1178,1847-03-31,143.0
1181 | 1179,1847-04-30,74.6
1182 | 1180,1847-05-31,125.7
1183 | 1181,1847-06-30,142.2
1184 | 1182,1847-07-31,87.1
1185 | 1183,1847-08-31,234.3
1186 | 1184,1847-09-30,268.2
1187 | 1185,1847-10-31,300.6
1188 | 1186,1847-11-30,231.5
1189 | 1187,1847-12-31,182.6
1190 | 1188,1848-01-31,265.2
1191 | 1189,1848-02-29,186.4
1192 | 1190,1848-03-31,181.2
1193 | 1191,1848-04-30,178.6
1194 | 1192,1848-05-31,170.5
1195 | 1193,1848-06-30,214.9
1196 | 1194,1848-07-31,232.0
1197 | 1195,1848-08-31,220.9
1198 | 1196,1848-09-30,167.3
1199 | 1197,1848-10-31,220.6
1200 | 1198,1848-11-30,191.0
1201 | 1199,1848-12-31,265.8
1202 | 1200,1849-01-31,298.3
1203 | 1201,1849-02-28,250.3
1204 | 1202,1849-03-31,182.7
1205 | 1203,1849-04-30,194.7
1206 | 1204,1849-05-31,153.2
1207 | 1205,1849-06-30,154.2
1208 | 1206,1849-07-31,148.2
1209 | 1207,1849-08-31,128.6
1210 | 1208,1849-09-30,178.0
1211 | 1209,1849-10-31,135.7
1212 | 1210,1849-11-30,188.2
1213 | 1211,1849-12-31,184.3
1214 | 1212,1850-01-31,148.2
1215 | 1213,1850-02-28,169.8
1216 | 1214,1850-03-31,156.8
1217 | 1215,1850-04-30,83.8
1218 | 1216,1850-05-31,117.0
1219 | 1217,1850-06-30,133.0
1220 | 1218,1850-07-31,74.3
1221 | 1219,1850-08-31,117.0
1222 | 1220,1850-09-30,163.7
1223 | 1221,1850-10-31,134.9
1224 | 1222,1850-11-30,104.1
1225 | 1223,1850-12-31,115.9
1226 | 1224,1851-01-31,143.5
1227 | 1225,1851-02-28,200.1
1228 | 1226,1851-03-31,122.8
1229 | 1227,1851-04-30,107.3
1230 | 1228,1851-05-31,118.9
1231 | 1229,1851-06-30,120.0
1232 | 1230,1851-07-31,68.6
1233 | 1231,1851-08-31,109.0
1234 | 1232,1851-09-30,128.9
1235 | 1233,1851-10-31,118.7
1236 | 1234,1851-11-30,96.8
1237 | 1235,1851-12-31,135.5
1238 | 1236,1852-01-31,129.9
1239 | 1237,1852-02-29,126.2
1240 | 1238,1852-03-31,116.2
1241 | 1239,1852-04-30,124.3
1242 | 1240,1852-05-31,104.3
1243 | 1241,1852-06-30,89.0
1244 | 1242,1852-07-31,80.0
1245 | 1243,1852-08-31,75.3
1246 | 1244,1852-09-30,71.2
1247 | 1245,1852-10-31,127.9
1248 | 1246,1852-11-30,103.1
1249 | 1247,1852-12-31,86.1
1250 | 1248,1853-01-31,77.9
1251 | 1249,1853-02-28,81.5
1252 | 1250,1853-03-31,71.6
1253 | 1251,1853-04-30,90.4
1254 | 1252,1853-05-31,65.9
1255 | 1253,1853-06-30,76.0
1256 | 1254,1853-07-31,87.2
1257 | 1255,1853-08-31,95.8
1258 | 1256,1853-09-30,63.7
1259 | 1257,1853-10-31,80.5
1260 | 1258,1853-11-30,54.7
1261 | 1259,1853-12-31,44.5
1262 | 1260,1854-01-31,29.3
1263 | 1261,1854-02-28,38.0
1264 | 1262,1854-03-31,39.4
1265 | 1263,1854-04-30,50.3
1266 | 1264,1854-05-31,45.6
1267 | 1265,1854-06-30,40.1
1268 | 1266,1854-07-31,35.5
1269 | 1267,1854-08-31,30.1
1270 | 1268,1854-09-30,42.6
1271 | 1269,1854-10-31,24.1
1272 | 1270,1854-11-30,53.5
1273 | 1271,1854-12-31,41.1
1274 | 1272,1855-01-31,23.4
1275 | 1273,1855-02-28,21.6
1276 | 1274,1855-03-31,33.0
1277 | 1275,1855-04-30,8.4
1278 | 1276,1855-05-31,17.3
1279 | 1277,1855-06-30,10.0
1280 | 1278,1855-07-31,0.7
1281 | 1279,1855-08-31,5.9
1282 | 1280,1855-09-30,0.0
1283 | 1281,1855-10-31,18.3
1284 | 1282,1855-11-30,7.9
1285 | 1283,1855-12-31,5.8
1286 | 1284,1856-01-31,1.0
1287 | 1285,1856-02-29,9.3
1288 | 1286,1856-03-31,0.7
1289 | 1287,1856-04-30,12.4
1290 | 1288,1856-05-31,0.0
1291 | 1289,1856-06-30,9.8
1292 | 1290,1856-07-31,8.8
1293 | 1291,1856-08-31,11.3
1294 | 1292,1856-09-30,8.4
1295 | 1293,1856-10-31,8.5
1296 | 1294,1856-11-30,14.7
1297 | 1295,1856-12-31,13.7
1298 | 1296,1857-01-31,26.0
1299 | 1297,1857-02-28,14.2
1300 | 1298,1857-03-31,10.0
1301 | 1299,1857-04-30,21.1
1302 | 1300,1857-05-31,54.2
1303 | 1301,1857-06-30,30.5
1304 | 1302,1857-07-31,42.2
1305 | 1303,1857-08-31,32.0
1306 | 1304,1857-09-30,80.6
1307 | 1305,1857-10-31,77.1
1308 | 1306,1857-11-30,59.6
1309 | 1307,1857-12-31,70.7
1310 | 1308,1858-01-31,74.2
1311 | 1309,1858-02-28,66.3
1312 | 1310,1858-03-31,109.2
1313 | 1311,1858-04-30,72.8
1314 | 1312,1858-05-31,78.6
1315 | 1313,1858-06-30,84.5
1316 | 1314,1858-07-31,107.7
1317 | 1315,1858-08-31,104.9
1318 | 1316,1858-09-30,152.2
1319 | 1317,1858-10-31,173.2
1320 | 1318,1858-11-30,98.6
1321 | 1319,1858-12-31,127.0
1322 | 1320,1859-01-31,159.0
1323 | 1321,1859-02-28,166.4
1324 | 1322,1859-03-31,171.5
1325 | 1323,1859-04-30,162.9
1326 | 1324,1859-05-31,172.8
1327 | 1325,1859-06-30,165.2
1328 | 1326,1859-07-31,180.7
1329 | 1327,1859-08-31,203.0
1330 | 1328,1859-09-30,200.9
1331 | 1329,1859-10-31,217.8
1332 | 1330,1859-11-30,184.6
1333 | 1331,1859-12-31,153.9
1334 | 1332,1860-01-31,156.6
1335 | 1333,1860-02-29,167.9
1336 | 1334,1860-03-31,188.0
1337 | 1335,1860-04-30,135.7
1338 | 1336,1860-05-31,203.5
1339 | 1337,1860-06-30,206.3
1340 | 1338,1860-07-31,221.9
1341 | 1339,1860-08-31,190.5
1342 | 1340,1860-09-30,175.2
1343 | 1341,1860-10-31,171.1
1344 | 1342,1860-11-30,186.0
1345 | 1343,1860-12-31,181.7
1346 | 1344,1861-01-31,118.5
1347 | 1345,1861-02-28,147.5
1348 | 1346,1861-03-31,192.0
1349 | 1347,1861-04-30,187.1
1350 | 1348,1861-05-31,107.8
1351 | 1349,1861-06-30,167.5
1352 | 1350,1861-07-31,148.2
1353 | 1351,1861-08-31,156.7
1354 | 1352,1861-09-30,151.9
1355 | 1353,1861-10-31,127.7
1356 | 1354,1861-11-30,102.0
1357 | 1355,1861-12-31,152.8
1358 | 1356,1862-01-31,119.9
1359 | 1357,1862-02-28,122.4
1360 | 1358,1862-03-31,82.7
1361 | 1359,1862-04-30,102.1
1362 | 1360,1862-05-31,122.4
1363 | 1361,1862-06-30,159.7
1364 | 1362,1862-07-31,139.5
1365 | 1363,1862-08-31,118.7
1366 | 1364,1862-09-30,126.4
1367 | 1365,1862-10-31,79.8
1368 | 1366,1862-11-30,96.0
1369 | 1367,1862-12-31,77.6
1370 | 1368,1863-01-31,91.8
1371 | 1369,1863-02-28,107.7
1372 | 1370,1863-03-31,126.0
1373 | 1371,1863-04-30,77.1
1374 | 1372,1863-05-31,102.1
1375 | 1373,1863-06-30,77.6
1376 | 1374,1863-07-31,62.0
1377 | 1375,1863-08-31,91.3
1378 | 1376,1863-09-30,41.7
1379 | 1377,1863-10-31,75.7
1380 | 1378,1863-11-30,71.6
1381 | 1379,1863-12-31,78.1
1382 | 1380,1864-01-31,109.7
1383 | 1381,1864-02-29,89.5
1384 | 1382,1864-03-31,125.9
1385 | 1383,1864-04-30,68.1
1386 | 1384,1864-05-31,77.1
1387 | 1385,1864-06-30,109.9
1388 | 1386,1864-07-31,103.8
1389 | 1387,1864-08-31,104.1
1390 | 1388,1864-09-30,54.2
1391 | 1389,1864-10-31,64.4
1392 | 1390,1864-11-30,109.3
1393 | 1391,1864-12-31,54.3
1394 | 1392,1865-01-31,92.5
1395 | 1393,1865-02-28,74.6
1396 | 1394,1865-03-31,75.0
1397 | 1395,1865-04-30,55.9
1398 | 1396,1865-05-31,65.6
1399 | 1397,1865-06-30,63.8
1400 | 1398,1865-07-31,50.8
1401 | 1399,1865-08-31,71.8
1402 | 1400,1865-09-30,41.0
1403 | 1401,1865-10-31,32.5
1404 | 1402,1865-11-30,46.7
1405 | 1403,1865-12-31,24.3
1406 | 1404,1866-01-31,60.0
1407 | 1405,1866-02-28,72.9
1408 | 1406,1866-03-31,46.6
1409 | 1407,1866-04-30,33.5
1410 | 1408,1866-05-31,24.6
1411 | 1409,1866-06-30,31.4
1412 | 1410,1866-07-31,17.6
1413 | 1411,1866-08-31,24.2
1414 | 1412,1866-09-30,13.9
1415 | 1413,1866-10-31,26.8
1416 | 1414,1866-11-30,17.2
1417 | 1415,1866-12-31,2.9
1418 | 1416,1867-01-31,0.0
1419 | 1417,1867-02-28,1.4
1420 | 1418,1867-03-31,17.4
1421 | 1419,1867-04-30,9.7
1422 | 1420,1867-05-31,5.5
1423 | 1421,1867-06-30,2.8
1424 | 1422,1867-07-31,9.5
1425 | 1423,1867-08-31,9.1
1426 | 1424,1867-09-30,18.7
1427 | 1425,1867-10-31,25.5
1428 | 1426,1867-11-30,18.1
1429 | 1427,1867-12-31,47.9
1430 | 1428,1868-01-31,25.9
1431 | 1429,1868-02-29,26.3
1432 | 1430,1868-03-31,44.0
1433 | 1431,1868-04-30,61.1
1434 | 1432,1868-05-31,44.5
1435 | 1433,1868-06-30,51.8
1436 | 1434,1868-07-31,48.3
1437 | 1435,1868-08-31,57.3
1438 | 1436,1868-09-30,78.7
1439 | 1437,1868-10-31,102.8
1440 | 1438,1868-11-30,98.7
1441 | 1439,1868-12-31,112.8
1442 | 1440,1869-01-31,101.6
1443 | 1441,1869-02-28,99.9
1444 | 1442,1869-03-31,88.0
1445 | 1443,1869-04-30,68.4
1446 | 1444,1869-05-31,173.4
1447 | 1445,1869-06-30,180.7
1448 | 1446,1869-07-31,98.8
1449 | 1447,1869-08-31,132.7
1450 | 1448,1869-09-30,134.5
1451 | 1449,1869-10-31,99.0
1452 | 1450,1869-11-30,130.2
1453 | 1451,1869-12-31,174.1
1454 | 1452,1870-01-31,129.0
1455 | 1453,1870-02-28,191.7
1456 | 1454,1870-03-31,262.7
1457 | 1455,1870-04-30,266.9
1458 | 1456,1870-05-31,293.6
1459 | 1457,1870-06-30,226.3
1460 | 1458,1870-07-31,220.9
1461 | 1459,1870-08-31,256.5
1462 | 1460,1870-09-30,226.8
1463 | 1461,1870-10-31,244.3
1464 | 1462,1870-11-30,246.0
1465 | 1463,1870-12-31,216.7
1466 | 1464,1871-01-31,147.2
1467 | 1465,1871-02-28,209.0
1468 | 1466,1871-03-31,238.7
1469 | 1467,1871-04-30,270.9
1470 | 1468,1871-05-31,242.6
1471 | 1469,1871-06-30,152.9
1472 | 1470,1871-07-31,171.8
1473 | 1471,1871-08-31,183.7
1474 | 1472,1871-09-30,133.9
1475 | 1473,1871-10-31,148.5
1476 | 1474,1871-11-30,175.7
1477 | 1475,1871-12-31,150.8
1478 | 1476,1872-01-31,132.7
1479 | 1477,1872-02-29,200.3
1480 | 1478,1872-03-31,147.5
1481 | 1479,1872-04-30,170.4
1482 | 1480,1872-05-31,179.5
1483 | 1481,1872-06-30,183.3
1484 | 1482,1872-07-31,176.0
1485 | 1483,1872-08-31,155.0
1486 | 1484,1872-09-30,191.1
1487 | 1485,1872-10-31,171.2
1488 | 1486,1872-11-30,186.8
1489 | 1487,1872-12-31,139.9
1490 | 1488,1873-01-31,144.6
1491 | 1489,1873-02-28,178.4
1492 | 1490,1873-03-31,164.0
1493 | 1491,1873-04-30,127.1
1494 | 1492,1873-05-31,80.0
1495 | 1493,1873-06-30,74.8
1496 | 1494,1873-07-31,111.7
1497 | 1495,1873-08-31,113.8
1498 | 1496,1873-09-30,78.7
1499 | 1497,1873-10-31,78.6
1500 | 1498,1873-11-30,92.5
1501 | 1499,1873-12-31,82.0
1502 | 1500,1874-01-31,101.4
1503 | 1501,1874-02-28,107.1
1504 | 1502,1874-03-31,77.5
1505 | 1503,1874-04-30,53.5
1506 | 1504,1874-05-31,74.6
1507 | 1505,1874-06-30,63.8
1508 | 1506,1874-07-31,113.2
1509 | 1507,1874-08-31,102.3
1510 | 1508,1874-09-30,46.8
1511 | 1509,1874-10-31,57.4
1512 | 1510,1874-11-30,48.2
1513 | 1511,1874-12-31,48.8
1514 | 1512,1875-01-31,24.4
1515 | 1513,1875-02-28,35.9
1516 | 1514,1875-03-31,56.4
1517 | 1515,1875-04-30,48.6
1518 | 1516,1875-05-31,19.2
1519 | 1517,1875-06-30,39.9
1520 | 1518,1875-07-31,20.9
1521 | 1519,1875-08-31,24.4
1522 | 1520,1875-09-30,4.0
1523 | 1521,1875-10-31,21.3
1524 | 1522,1875-11-30,29.6
1525 | 1523,1875-12-31,16.5
1526 | 1524,1876-01-31,23.9
1527 | 1525,1876-02-29,25.1
1528 | 1526,1876-03-31,51.1
1529 | 1527,1876-04-30,3.9
1530 | 1528,1876-05-31,8.5
1531 | 1529,1876-06-30,2.7
1532 | 1530,1876-07-31,25.5
1533 | 1531,1876-08-31,14.8
1534 | 1532,1876-09-30,16.6
1535 | 1533,1876-10-31,23.9
1536 | 1534,1876-11-30,16.6
1537 | 1535,1876-12-31,13.6
1538 | 1536,1877-01-31,40.6
1539 | 1537,1877-02-28,14.5
1540 | 1538,1877-03-31,19.9
1541 | 1539,1877-04-30,26.3
1542 | 1540,1877-05-31,36.1
1543 | 1541,1877-06-30,23.6
1544 | 1542,1877-07-31,10.0
1545 | 1543,1877-08-31,10.5
1546 | 1544,1877-09-30,28.1
1547 | 1545,1877-10-31,11.2
1548 | 1546,1877-11-30,23.6
1549 | 1547,1877-12-31,3.5
1550 | 1548,1878-01-31,5.5
1551 | 1549,1878-02-28,11.0
1552 | 1550,1878-03-31,13.1
1553 | 1551,1878-04-30,0.2
1554 | 1552,1878-05-31,9.9
1555 | 1553,1878-06-30,10.7
1556 | 1554,1878-07-31,0.2
1557 | 1555,1878-08-31,0.0
1558 | 1556,1878-09-30,8.8
1559 | 1557,1878-10-31,1.9
1560 | 1558,1878-11-30,6.8
1561 | 1559,1878-12-31,0.9
1562 | 1560,1879-01-31,1.6
1563 | 1561,1879-02-28,0.9
1564 | 1562,1879-03-31,0.0
1565 | 1563,1879-04-30,10.4
1566 | 1564,1879-05-31,4.0
1567 | 1565,1879-06-30,8.0
1568 | 1566,1879-07-31,12.6
1569 | 1567,1879-08-31,17.9
1570 | 1568,1879-09-30,10.1
1571 | 1569,1879-10-31,20.4
1572 | 1570,1879-11-30,21.9
1573 | 1571,1879-12-31,12.2
1574 | 1572,1880-01-31,40.1
1575 | 1573,1880-02-29,45.4
1576 | 1574,1880-03-31,32.1
1577 | 1575,1880-04-30,32.4
1578 | 1576,1880-05-31,39.1
1579 | 1577,1880-06-30,56.9
1580 | 1578,1880-07-31,36.5
1581 | 1579,1880-08-31,80.3
1582 | 1580,1880-09-30,110.1
1583 | 1581,1880-10-31,71.7
1584 | 1582,1880-11-30,51.1
1585 | 1583,1880-12-31,49.4
1586 | 1584,1881-01-31,60.6
1587 | 1585,1881-02-28,88.6
1588 | 1586,1881-03-31,85.8
1589 | 1587,1881-04-30,86.1
1590 | 1588,1881-05-31,72.5
1591 | 1589,1881-06-30,100.8
1592 | 1590,1881-07-31,128.2
1593 | 1591,1881-08-31,97.3
1594 | 1592,1881-09-30,88.6
1595 | 1593,1881-10-31,107.3
1596 | 1594,1881-11-30,91.5
1597 | 1595,1881-12-31,78.8
1598 | 1596,1882-01-31,75.0
1599 | 1597,1882-02-28,115.9
1600 | 1598,1882-03-31,111.5
1601 | 1599,1882-04-30,159.6
1602 | 1600,1882-05-31,106.9
1603 | 1601,1882-06-30,75.4
1604 | 1602,1882-07-31,75.7
1605 | 1603,1882-08-31,67.5
1606 | 1604,1882-09-30,96.1
1607 | 1605,1882-10-31,98.6
1608 | 1606,1882-11-30,140.6
1609 | 1607,1882-12-31,69.7
1610 | 1608,1883-01-31,101.0
1611 | 1609,1883-02-28,78.2
1612 | 1610,1883-03-31,71.3
1613 | 1611,1883-04-30,136.8
1614 | 1612,1883-05-31,52.5
1615 | 1613,1883-06-30,127.2
1616 | 1614,1883-07-31,134.4
1617 | 1615,1883-08-31,76.6
1618 | 1616,1883-09-30,87.8
1619 | 1617,1883-10-31,139.6
1620 | 1618,1883-11-30,140.8
1621 | 1619,1883-12-31,126.5
1622 | 1620,1884-01-31,152.6
1623 | 1621,1884-02-29,144.8
1624 | 1622,1884-03-31,145.8
1625 | 1623,1884-04-30,126.9
1626 | 1624,1884-05-31,110.9
1627 | 1625,1884-06-30,85.3
1628 | 1626,1884-07-31,88.5
1629 | 1627,1884-08-31,93.1
1630 | 1628,1884-09-30,103.1
1631 | 1629,1884-10-31,79.6
1632 | 1630,1884-11-30,60.9
1633 | 1631,1884-12-31,78.6
1634 | 1632,1885-01-31,71.4
1635 | 1633,1885-02-28,119.7
1636 | 1634,1885-03-31,82.9
1637 | 1635,1885-04-30,91.7
1638 | 1636,1885-05-31,121.6
1639 | 1637,1885-06-30,139.5
1640 | 1638,1885-07-31,110.9
1641 | 1639,1885-08-31,83.5
1642 | 1640,1885-09-30,66.0
1643 | 1641,1885-10-31,64.5
1644 | 1642,1885-11-30,51.6
1645 | 1643,1885-12-31,36.3
1646 | 1644,1886-01-31,49.8
1647 | 1645,1886-02-28,43.2
1648 | 1646,1886-03-31,95.5
1649 | 1647,1886-04-30,72.9
1650 | 1648,1886-05-31,51.2
1651 | 1649,1886-06-30,45.3
1652 | 1650,1886-07-31,50.4
1653 | 1651,1886-08-31,28.1
1654 | 1652,1886-09-30,35.7
1655 | 1653,1886-10-31,14.4
1656 | 1654,1886-11-30,0.6
1657 | 1655,1886-12-31,21.7
1658 | 1656,1887-01-31,17.0
1659 | 1657,1887-02-28,22.0
1660 | 1658,1887-03-31,7.1
1661 | 1659,1887-04-30,11.6
1662 | 1660,1887-05-31,33.4
1663 | 1661,1887-06-30,26.1
1664 | 1662,1887-07-31,38.9
1665 | 1663,1887-08-31,35.7
1666 | 1664,1887-09-30,12.4
1667 | 1665,1887-10-31,11.0
1668 | 1666,1887-11-30,11.5
1669 | 1667,1887-12-31,34.5
1670 | 1668,1888-01-31,21.2
1671 | 1669,1888-02-29,11.8
1672 | 1670,1888-03-31,13.1
1673 | 1671,1888-04-30,8.5
1674 | 1672,1888-05-31,11.7
1675 | 1673,1888-06-30,11.8
1676 | 1674,1888-07-31,5.0
1677 | 1675,1888-08-31,4.6
1678 | 1676,1888-09-30,14.6
1679 | 1677,1888-10-31,3.4
1680 | 1678,1888-11-30,17.9
1681 | 1679,1888-12-31,11.1
1682 | 1680,1889-01-31,1.3
1683 | 1681,1889-02-28,14.2
1684 | 1682,1889-03-31,11.1
1685 | 1683,1889-04-30,7.3
1686 | 1684,1889-05-31,4.0
1687 | 1685,1889-06-30,10.8
1688 | 1686,1889-07-31,15.8
1689 | 1687,1889-08-31,34.3
1690 | 1688,1889-09-30,10.9
1691 | 1689,1889-10-31,3.4
1692 | 1690,1889-11-30,0.3
1693 | 1691,1889-12-31,11.2
1694 | 1692,1890-01-31,8.8
1695 | 1693,1890-02-28,1.1
1696 | 1694,1890-03-31,8.5
1697 | 1695,1890-04-30,2.7
1698 | 1696,1890-05-31,7.9
1699 | 1697,1890-06-30,2.2
1700 | 1698,1890-07-31,19.3
1701 | 1699,1890-08-31,14.2
1702 | 1700,1890-09-30,28.6
1703 | 1701,1890-10-31,18.6
1704 | 1702,1890-11-30,15.9
1705 | 1703,1890-12-31,13.1
1706 | 1704,1891-01-31,22.5
1707 | 1705,1891-02-28,36.9
1708 | 1706,1891-03-31,17.2
1709 | 1707,1891-04-30,34.2
1710 | 1708,1891-05-31,68.5
1711 | 1709,1891-06-30,80.4
1712 | 1710,1891-07-31,98.0
1713 | 1711,1891-08-31,55.1
1714 | 1712,1891-09-30,89.7
1715 | 1713,1891-10-31,86.0
1716 | 1714,1891-11-30,69.8
1717 | 1715,1891-12-31,54.3
1718 | 1716,1892-01-31,115.2
1719 | 1717,1892-02-29,126.1
1720 | 1718,1892-03-31,83.3
1721 | 1719,1892-04-30,116.0
1722 | 1720,1892-05-31,132.7
1723 | 1721,1892-06-30,127.3
1724 | 1722,1892-07-31,127.6
1725 | 1723,1892-08-31,169.0
1726 | 1724,1892-09-30,104.7
1727 | 1725,1892-10-31,117.5
1728 | 1726,1892-11-30,108.9
1729 | 1727,1892-12-31,131.0
1730 | 1728,1893-01-31,125.0
1731 | 1729,1893-02-28,121.6
1732 | 1730,1893-03-31,109.5
1733 | 1731,1893-04-30,146.8
1734 | 1732,1893-05-31,141.3
1735 | 1733,1893-06-30,149.8
1736 | 1734,1893-07-31,147.6
1737 | 1735,1893-08-31,215.4
1738 | 1736,1893-09-30,129.9
1739 | 1737,1893-10-31,133.3
1740 | 1738,1893-11-30,125.2
1741 | 1739,1893-12-31,156.4
1742 | 1740,1894-01-31,138.6
1743 | 1741,1894-02-28,141.0
1744 | 1742,1894-03-31,87.1
1745 | 1743,1894-04-30,136.0
1746 | 1744,1894-05-31,168.7
1747 | 1745,1894-06-30,164.8
1748 | 1746,1894-07-31,176.7
1749 | 1747,1894-08-31,117.1
1750 | 1748,1894-09-30,110.0
1751 | 1749,1894-10-31,125.7
1752 | 1750,1894-11-30,94.3
1753 | 1751,1894-12-31,100.1
1754 | 1752,1895-01-31,105.4
1755 | 1753,1895-02-28,112.0
1756 | 1754,1895-03-31,101.6
1757 | 1755,1895-04-30,128.2
1758 | 1756,1895-05-31,112.5
1759 | 1757,1895-06-30,119.1
1760 | 1758,1895-07-31,79.7
1761 | 1759,1895-08-31,114.9
1762 | 1760,1895-09-30,96.1
1763 | 1761,1895-10-31,113.2
1764 | 1762,1895-11-30,78.6
1765 | 1763,1895-12-31,117.9
1766 | 1764,1896-01-31,48.4
1767 | 1765,1896-02-29,95.7
1768 | 1766,1896-03-31,86.5
1769 | 1767,1896-04-30,73.0
1770 | 1768,1896-05-31,46.1
1771 | 1769,1896-06-30,81.7
1772 | 1770,1896-07-31,75.2
1773 | 1771,1896-08-31,45.4
1774 | 1772,1896-09-30,102.2
1775 | 1773,1896-10-31,47.9
1776 | 1774,1896-11-30,63.3
1777 | 1775,1896-12-31,71.0
1778 | 1776,1897-01-31,67.6
1779 | 1777,1897-02-28,49.0
1780 | 1778,1897-03-31,48.7
1781 | 1779,1897-04-30,51.7
1782 | 1780,1897-05-31,33.3
1783 | 1781,1897-06-30,18.9
1784 | 1782,1897-07-31,46.1
1785 | 1783,1897-08-31,36.4
1786 | 1784,1897-09-30,80.4
1787 | 1785,1897-10-31,23.9
1788 | 1786,1897-11-30,14.0
1789 | 1787,1897-12-31,55.5
1790 | 1788,1898-01-31,50.4
1791 | 1789,1898-02-28,60.7
1792 | 1790,1898-03-31,63.9
1793 | 1791,1898-04-30,24.2
1794 | 1792,1898-05-31,43.0
1795 | 1793,1898-06-30,37.1
1796 | 1794,1898-07-31,15.0
1797 | 1795,1898-08-31,52.3
1798 | 1796,1898-09-30,58.1
1799 | 1797,1898-10-31,57.1
1800 | 1798,1898-11-30,51.6
1801 | 1799,1898-12-31,21.1
1802 | 1800,1899-01-31,32.4
1803 | 1801,1899-02-28,15.3
1804 | 1802,1899-03-31,30.3
1805 | 1803,1899-04-30,23.6
1806 | 1804,1899-05-31,12.9
1807 | 1805,1899-06-30,34.1
1808 | 1806,1899-07-31,22.5
1809 | 1807,1899-08-31,4.9
1810 | 1808,1899-09-30,14.0
1811 | 1809,1899-10-31,21.7
1812 | 1810,1899-11-30,13.0
1813 | 1811,1899-12-31,17.6
1814 | 1812,1900-01-31,15.7
1815 | 1813,1900-02-28,22.8
1816 | 1814,1900-03-31,14.4
1817 | 1815,1900-04-30,26.8
1818 | 1816,1900-05-31,25.3
1819 | 1817,1900-06-30,20.1
1820 | 1818,1900-07-31,13.9
1821 | 1819,1900-08-31,7.1
1822 | 1820,1900-09-30,13.9
1823 | 1821,1900-10-31,21.6
1824 | 1822,1900-11-30,7.5
1825 | 1823,1900-12-31,0.5
1826 | 1824,1901-01-31,0.4
1827 | 1825,1901-02-28,4.0
1828 | 1826,1901-03-31,7.4
1829 | 1827,1901-04-30,0.0
1830 | 1828,1901-05-31,17.0
1831 | 1829,1901-06-30,9.7
1832 | 1830,1901-07-31,1.2
1833 | 1831,1901-08-31,1.7
1834 | 1832,1901-09-30,0.9
1835 | 1833,1901-10-31,6.3
1836 | 1834,1901-11-30,6.4
1837 | 1835,1901-12-31,0.0
1838 | 1836,1902-01-31,9.3
1839 | 1837,1902-02-28,0.0
1840 | 1838,1902-03-31,20.7
1841 | 1839,1902-04-30,0.0
1842 | 1840,1902-05-31,4.7
1843 | 1841,1902-06-30,2.4
1844 | 1842,1902-07-31,1.5
1845 | 1843,1902-08-31,3.8
1846 | 1844,1902-09-30,12.6
1847 | 1845,1902-10-31,27.2
1848 | 1846,1902-11-30,17.2
1849 | 1847,1902-12-31,1.8
1850 | 1848,1903-01-31,13.9
1851 | 1849,1903-02-28,28.4
1852 | 1850,1903-03-31,22.5
1853 | 1851,1903-04-30,43.5
1854 | 1852,1903-05-31,24.3
1855 | 1853,1903-06-30,27.2
1856 | 1854,1903-07-31,46.4
1857 | 1855,1903-08-31,48.0
1858 | 1856,1903-09-30,18.5
1859 | 1857,1903-10-31,64.8
1860 | 1858,1903-11-30,74.2
1861 | 1859,1903-12-31,76.2
1862 | 1860,1904-01-31,52.6
1863 | 1861,1904-02-29,40.8
1864 | 1862,1904-03-31,61.9
1865 | 1863,1904-04-30,71.6
1866 | 1864,1904-05-31,65.7
1867 | 1865,1904-06-30,69.8
1868 | 1866,1904-07-31,84.3
1869 | 1867,1904-08-31,97.1
1870 | 1868,1904-09-30,50.2
1871 | 1869,1904-10-31,90.4
1872 | 1870,1904-11-30,63.4
1873 | 1871,1904-12-31,91.1
1874 | 1872,1905-01-31,91.2
1875 | 1873,1905-02-28,143.0
1876 | 1874,1905-03-31,94.3
1877 | 1875,1905-04-30,65.5
1878 | 1876,1905-05-31,79.9
1879 | 1877,1905-06-30,81.6
1880 | 1878,1905-07-31,121.6
1881 | 1879,1905-08-31,98.1
1882 | 1880,1905-09-30,91.8
1883 | 1881,1905-10-31,131.2
1884 | 1882,1905-11-30,178.7
1885 | 1883,1905-12-31,92.5
1886 | 1884,1906-01-31,75.8
1887 | 1885,1906-02-28,52.3
1888 | 1886,1906-03-31,107.4
1889 | 1887,1906-04-30,92.2
1890 | 1888,1906-05-31,96.2
1891 | 1889,1906-06-30,105.3
1892 | 1890,1906-07-31,172.7
1893 | 1891,1906-08-31,79.6
1894 | 1892,1906-09-30,93.5
1895 | 1893,1906-10-31,29.7
1896 | 1894,1906-11-30,64.8
1897 | 1895,1906-12-31,108.0
1898 | 1896,1907-01-31,127.4
1899 | 1897,1907-02-28,180.3
1900 | 1898,1907-03-31,101.1
1901 | 1899,1907-04-30,87.6
1902 | 1900,1907-05-31,71.4
1903 | 1901,1907-06-30,67.2
1904 | 1902,1907-07-31,82.9
1905 | 1903,1907-08-31,90.5
1906 | 1904,1907-09-30,141.7
1907 | 1905,1907-10-31,109.1
1908 | 1906,1907-11-30,102.5
1909 | 1907,1907-12-31,78.9
1910 | 1908,1908-01-31,65.4
1911 | 1909,1908-02-29,56.6
1912 | 1910,1908-03-31,47.9
1913 | 1911,1908-04-30,96.1
1914 | 1912,1908-05-31,68.0
1915 | 1913,1908-06-30,80.2
1916 | 1914,1908-07-31,65.8
1917 | 1915,1908-08-31,150.9
1918 | 1916,1908-09-30,144.9
1919 | 1917,1908-10-31,53.9
1920 | 1918,1908-11-30,75.9
1921 | 1919,1908-12-31,65.9
1922 | 1920,1909-01-31,94.6
1923 | 1921,1909-02-28,77.6
1924 | 1922,1909-03-31,110.5
1925 | 1923,1909-04-30,53.7
1926 | 1924,1909-05-31,60.0
1927 | 1925,1909-06-30,37.8
1928 | 1926,1909-07-31,59.6
1929 | 1927,1909-08-31,38.5
1930 | 1928,1909-09-30,64.7
1931 | 1929,1909-10-31,97.4
1932 | 1930,1909-11-30,93.0
1933 | 1931,1909-12-31,90.2
1934 | 1932,1910-01-31,44.0
1935 | 1933,1910-02-28,52.5
1936 | 1934,1910-03-31,35.7
1937 | 1935,1910-04-30,14.1
1938 | 1936,1910-05-31,36.9
1939 | 1937,1910-06-30,20.5
1940 | 1938,1910-07-31,23.5
1941 | 1939,1910-08-31,19.3
1942 | 1940,1910-09-30,43.7
1943 | 1941,1910-10-31,63.9
1944 | 1942,1910-11-30,8.3
1945 | 1943,1910-12-31,9.6
1946 | 1944,1911-01-31,5.6
1947 | 1945,1911-02-28,15.0
1948 | 1946,1911-03-31,13.0
1949 | 1947,1911-04-30,27.6
1950 | 1948,1911-05-31,15.1
1951 | 1949,1911-06-30,3.7
1952 | 1950,1911-07-31,5.9
1953 | 1951,1911-08-31,6.7
1954 | 1952,1911-09-30,6.7
1955 | 1953,1911-10-31,4.5
1956 | 1954,1911-11-30,7.0
1957 | 1955,1911-12-31,3.7
1958 | 1956,1912-01-31,0.4
1959 | 1957,1912-02-29,0.0
1960 | 1958,1912-03-31,8.2
1961 | 1959,1912-04-30,7.6
1962 | 1960,1912-05-31,7.4
1963 | 1961,1912-06-30,6.9
1964 | 1962,1912-07-31,4.9
1965 | 1963,1912-08-31,0.5
1966 | 1964,1912-09-30,15.9
1967 | 1965,1912-10-31,7.6
1968 | 1966,1912-11-30,1.9
1969 | 1967,1912-12-31,10.6
1970 | 1968,1913-01-31,3.8
1971 | 1969,1913-02-28,4.8
1972 | 1970,1913-03-31,0.8
1973 | 1971,1913-04-30,1.6
1974 | 1972,1913-05-31,0.0
1975 | 1973,1913-06-30,0.0
1976 | 1974,1913-07-31,2.9
1977 | 1975,1913-08-31,0.4
1978 | 1976,1913-09-30,2.0
1979 | 1977,1913-10-31,5.2
1980 | 1978,1913-11-30,1.2
1981 | 1979,1913-12-31,6.3
1982 | 1980,1914-01-31,4.7
1983 | 1981,1914-02-28,4.4
1984 | 1982,1914-03-31,5.3
1985 | 1983,1914-04-30,28.9
1986 | 1984,1914-05-31,8.7
1987 | 1985,1914-06-30,19.1
1988 | 1986,1914-07-31,9.1
1989 | 1987,1914-08-31,12.9
1990 | 1988,1914-09-30,21.2
1991 | 1989,1914-10-31,13.7
1992 | 1990,1914-11-30,27.3
1993 | 1991,1914-12-31,37.3
1994 | 1992,1915-01-31,38.5
1995 | 1993,1915-02-28,70.4
1996 | 1994,1915-03-31,64.8
1997 | 1995,1915-04-30,68.9
1998 | 1996,1915-05-31,55.1
1999 | 1997,1915-06-30,114.7
2000 | 1998,1915-07-31,119.4
2001 | 1999,1915-08-31,116.0
2002 | 2000,1915-09-30,82.4
2003 | 2001,1915-10-31,89.1
2004 | 2002,1915-11-30,70.9
2005 | 2003,1915-12-31,57.5
2006 | 2004,1916-01-31,75.5
2007 | 2005,1916-02-29,92.4
2008 | 2006,1916-03-31,111.7
2009 | 2007,1916-04-30,119.8
2010 | 2008,1916-05-31,124.0
2011 | 2009,1916-06-30,112.9
2012 | 2010,1916-07-31,89.3
2013 | 2011,1916-08-31,58.6
2014 | 2012,1916-09-30,75.1
2015 | 2013,1916-10-31,84.5
2016 | 2014,1916-11-30,109.5
2017 | 2015,1916-12-31,88.3
2018 | 2016,1917-01-31,124.5
2019 | 2017,1917-02-28,119.9
2020 | 2018,1917-03-31,158.0
2021 | 2019,1917-04-30,124.6
2022 | 2020,1917-05-31,190.2
2023 | 2021,1917-06-30,191.4
2024 | 2022,1917-07-31,199.7
2025 | 2023,1917-08-31,257.7
2026 | 2024,1917-09-30,215.6
2027 | 2025,1917-10-31,120.3
2028 | 2026,1917-11-30,160.7
2029 | 2027,1917-12-31,215.5
2030 | 2028,1918-01-31,160.0
2031 | 2029,1918-02-28,108.7
2032 | 2030,1918-03-31,120.4
2033 | 2031,1918-04-30,134.1
2034 | 2032,1918-05-31,127.9
2035 | 2033,1918-06-30,99.0
2036 | 2034,1918-07-31,179.5
2037 | 2035,1918-08-31,169.6
2038 | 2036,1918-09-30,133.2
2039 | 2037,1918-10-31,141.7
2040 | 2038,1918-11-30,139.0
2041 | 2039,1918-12-31,98.5
2042 | 2040,1919-01-31,80.2
2043 | 2041,1919-02-28,132.5
2044 | 2042,1919-03-31,110.9
2045 | 2043,1919-04-30,86.3
2046 | 2044,1919-05-31,146.9
2047 | 2045,1919-06-30,185.3
2048 | 2046,1919-07-31,107.8
2049 | 2047,1919-08-31,115.1
2050 | 2048,1919-09-30,91.1
2051 | 2049,1919-10-31,88.0
2052 | 2050,1919-11-30,69.9
2053 | 2051,1919-12-31,58.0
2054 | 2052,1920-01-31,85.3
2055 | 2053,1920-02-29,89.8
2056 | 2054,1920-03-31,116.9
2057 | 2055,1920-04-30,24.7
2058 | 2056,1920-05-31,55.4
2059 | 2057,1920-06-30,64.6
2060 | 2058,1920-07-31,45.7
2061 | 2059,1920-08-31,31.9
2062 | 2060,1920-09-30,60.5
2063 | 2061,1920-10-31,82.7
2064 | 2062,1920-11-30,45.3
2065 | 2063,1920-12-31,49.8
2066 | 2064,1921-01-31,52.5
2067 | 2065,1921-02-28,47.1
2068 | 2066,1921-03-31,44.5
2069 | 2067,1921-04-30,54.0
2070 | 2068,1921-05-31,37.0
2071 | 2069,1921-06-30,56.2
2072 | 2070,1921-07-31,69.7
2073 | 2071,1921-08-31,38.0
2074 | 2072,1921-09-30,29.6
2075 | 2073,1921-10-31,30.3
2076 | 2074,1921-11-30,29.6
2077 | 2075,1921-12-31,33.9
2078 | 2076,1922-01-31,19.7
2079 | 2077,1922-02-28,43.9
2080 | 2078,1922-03-31,91.3
2081 | 2079,1922-04-30,18.3
2082 | 2080,1922-05-31,13.4
2083 | 2081,1922-06-30,9.8
2084 | 2082,1922-07-31,18.3
2085 | 2083,1922-08-31,10.9
2086 | 2084,1922-09-30,7.8
2087 | 2085,1922-10-31,10.4
2088 | 2086,1922-11-30,12.2
2089 | 2087,1922-12-31,29.0
2090 | 2088,1923-01-31,7.5
2091 | 2089,1923-02-28,2.5
2092 | 2090,1923-03-31,5.5
2093 | 2091,1923-04-30,10.2
2094 | 2092,1923-05-31,5.3
2095 | 2093,1923-06-30,15.3
2096 | 2094,1923-07-31,5.9
2097 | 2095,1923-08-31,0.8
2098 | 2096,1923-09-30,22.1
2099 | 2097,1923-10-31,19.5
2100 | 2098,1923-11-30,16.7
2101 | 2099,1923-12-31,4.6
2102 | 2100,1924-01-31,0.8
2103 | 2101,1924-02-29,8.5
2104 | 2102,1924-03-31,3.0
2105 | 2103,1924-04-30,18.9
2106 | 2104,1924-05-31,34.6
2107 | 2105,1924-06-30,40.0
2108 | 2106,1924-07-31,46.9
2109 | 2107,1924-08-31,32.1
2110 | 2108,1924-09-30,41.9
2111 | 2109,1924-10-31,42.6
2112 | 2110,1924-11-30,37.5
2113 | 2111,1924-12-31,27.5
2114 | 2112,1925-01-31,9.1
2115 | 2113,1925-02-28,38.6
2116 | 2114,1925-03-31,30.0
2117 | 2115,1925-04-30,53.0
2118 | 2116,1925-05-31,71.4
2119 | 2117,1925-06-30,79.1
2120 | 2118,1925-07-31,64.2
2121 | 2119,1925-08-31,63.3
2122 | 2120,1925-09-30,100.3
2123 | 2121,1925-10-31,115.3
2124 | 2122,1925-11-30,97.6
2125 | 2123,1925-12-31,164.4
2126 | 2124,1926-01-31,119.7
2127 | 2125,1926-02-28,116.4
2128 | 2126,1926-03-31,104.2
2129 | 2127,1926-04-30,64.2
2130 | 2128,1926-05-31,107.0
2131 | 2129,1926-06-30,122.4
2132 | 2130,1926-07-31,87.2
2133 | 2131,1926-08-31,102.7
2134 | 2132,1926-09-30,101.4
2135 | 2133,1926-10-31,119.3
2136 | 2134,1926-11-30,100.8
2137 | 2135,1926-12-31,132.4
2138 | 2136,1927-01-31,136.0
2139 | 2137,1927-02-28,155.1
2140 | 2138,1927-03-31,116.1
2141 | 2139,1927-04-30,155.8
2142 | 2140,1927-05-31,131.9
2143 | 2141,1927-06-30,98.5
2144 | 2142,1927-07-31,91.6
2145 | 2143,1927-08-31,89.6
2146 | 2144,1927-09-30,114.1
2147 | 2145,1927-10-31,105.2
2148 | 2146,1927-11-30,112.1
2149 | 2147,1927-12-31,75.3
2150 | 2148,1928-01-31,139.2
2151 | 2149,1928-02-29,122.4
2152 | 2150,1928-03-31,142.4
2153 | 2151,1928-04-30,134.3
2154 | 2152,1928-05-31,128.3
2155 | 2153,1928-06-30,152.4
2156 | 2154,1928-07-31,163.4
2157 | 2155,1928-08-31,139.6
2158 | 2156,1928-09-30,149.6
2159 | 2157,1928-10-31,102.3
2160 | 2158,1928-11-30,83.9
2161 | 2159,1928-12-31,98.3
2162 | 2160,1929-01-31,114.8
2163 | 2161,1929-02-28,104.7
2164 | 2162,1929-03-31,83.8
2165 | 2163,1929-04-30,87.9
2166 | 2164,1929-05-31,97.0
2167 | 2165,1929-06-30,119.9
2168 | 2166,1929-07-31,117.1
2169 | 2167,1929-08-31,109.6
2170 | 2168,1929-09-30,57.3
2171 | 2169,1929-10-31,90.1
2172 | 2170,1929-11-30,135.1
2173 | 2171,1929-12-31,179.9
2174 | 2172,1930-01-31,108.8
2175 | 2173,1930-02-28,83.1
2176 | 2174,1930-03-31,58.4
2177 | 2175,1930-04-30,63.7
2178 | 2176,1930-05-31,61.4
2179 | 2177,1930-06-30,48.0
2180 | 2178,1930-07-31,36.5
2181 | 2179,1930-08-31,41.5
2182 | 2180,1930-09-30,53.5
2183 | 2181,1930-10-31,57.2
2184 | 2182,1930-11-30,59.4
2185 | 2183,1930-12-31,43.0
2186 | 2184,1931-01-31,24.4
2187 | 2185,1931-02-28,71.8
2188 | 2186,1931-03-31,50.1
2189 | 2187,1931-04-30,52.0
2190 | 2188,1931-05-31,40.8
2191 | 2189,1931-06-30,25.5
2192 | 2190,1931-07-31,29.0
2193 | 2191,1931-08-31,21.6
2194 | 2192,1931-09-30,31.7
2195 | 2193,1931-10-31,16.6
2196 | 2194,1931-11-30,31.2
2197 | 2195,1931-12-31,29.7
2198 | 2196,1932-01-31,20.1
2199 | 2197,1932-02-29,17.6
2200 | 2198,1932-03-31,18.8
2201 | 2199,1932-04-30,18.7
2202 | 2200,1932-05-31,29.7
2203 | 2201,1932-06-30,36.9
2204 | 2202,1932-07-31,16.0
2205 | 2203,1932-08-31,11.3
2206 | 2204,1932-09-30,6.6
2207 | 2205,1932-10-31,14.8
2208 | 2206,1932-11-30,13.7
2209 | 2207,1932-12-31,18.5
2210 | 2208,1933-01-31,20.4
2211 | 2209,1933-02-28,36.9
2212 | 2210,1933-03-31,16.8
2213 | 2211,1933-04-30,4.9
2214 | 2212,1933-05-31,5.3
2215 | 2213,1933-06-30,8.7
2216 | 2214,1933-07-31,4.7
2217 | 2215,1933-08-31,0.4
2218 | 2216,1933-09-30,8.5
2219 | 2217,1933-10-31,5.0
2220 | 2218,1933-11-30,1.0
2221 | 2219,1933-12-31,0.4
2222 | 2220,1934-01-31,5.7
2223 | 2221,1934-02-28,12.9
2224 | 2222,1934-03-31,7.2
2225 | 2223,1934-04-30,18.8
2226 | 2224,1934-05-31,32.9
2227 | 2225,1934-06-30,11.2
2228 | 2226,1934-07-31,15.3
2229 | 2227,1934-08-31,13.9
2230 | 2228,1934-09-30,6.7
2231 | 2229,1934-10-31,9.5
2232 | 2230,1934-11-30,14.5
2233 | 2231,1934-12-31,25.7
2234 | 2232,1935-01-31,31.1
2235 | 2233,1935-02-28,34.1
2236 | 2234,1935-03-31,38.5
2237 | 2235,1935-04-30,20.4
2238 | 2236,1935-05-31,45.4
2239 | 2237,1935-06-30,76.2
2240 | 2238,1935-07-31,56.5
2241 | 2239,1935-08-31,50.2
2242 | 2240,1935-09-30,70.1
2243 | 2241,1935-10-31,88.7
2244 | 2242,1935-11-30,107.0
2245 | 2243,1935-12-31,102.5
2246 | 2244,1936-01-31,104.7
2247 | 2245,1936-02-29,123.9
2248 | 2246,1936-03-31,128.4
2249 | 2247,1936-04-30,124.8
2250 | 2248,1936-05-31,90.9
2251 | 2249,1936-06-30,116.7
2252 | 2250,1936-07-31,87.2
2253 | 2251,1936-08-31,145.0
2254 | 2252,1936-09-30,126.7
2255 | 2253,1936-10-31,148.2
2256 | 2254,1936-11-30,192.3
2257 | 2255,1936-12-31,205.6
2258 | 2256,1937-01-31,220.9
2259 | 2257,1937-02-28,214.1
2260 | 2258,1937-03-31,139.7
2261 | 2259,1937-04-30,182.2
2262 | 2260,1937-05-31,194.4
2263 | 2261,1937-06-30,217.1
2264 | 2262,1937-07-31,241.8
2265 | 2263,1937-08-31,229.5
2266 | 2264,1937-09-30,167.9
2267 | 2265,1937-10-31,208.2
2268 | 2266,1937-11-30,123.9
2269 | 2267,1937-12-31,147.9
2270 | 2268,1938-01-31,164.0
2271 | 2269,1938-02-28,198.6
2272 | 2270,1938-03-31,144.2
2273 | 2271,1938-04-30,168.4
2274 | 2272,1938-05-31,212.3
2275 | 2273,1938-06-30,162.6
2276 | 2274,1938-07-31,275.6
2277 | 2275,1938-08-31,192.8
2278 | 2276,1938-09-30,149.3
2279 | 2277,1938-10-31,165.3
2280 | 2278,1938-11-30,203.6
2281 | 2279,1938-12-31,154.7
2282 | 2280,1939-01-31,133.9
2283 | 2281,1939-02-28,129.0
2284 | 2282,1939-03-31,107.8
2285 | 2283,1939-04-30,181.9
2286 | 2284,1939-05-31,197.3
2287 | 2285,1939-06-30,168.3
2288 | 2286,1939-07-31,162.6
2289 | 2287,1939-08-31,176.3
2290 | 2288,1939-09-30,187.7
2291 | 2289,1939-10-31,146.9
2292 | 2290,1939-11-30,113.7
2293 | 2291,1939-12-31,70.2
2294 | 2292,1940-01-31,84.1
2295 | 2293,1940-02-29,99.1
2296 | 2294,1940-03-31,138.9
2297 | 2295,1940-04-30,101.1
2298 | 2296,1940-05-31,90.6
2299 | 2297,1940-06-30,139.8
2300 | 2298,1940-07-31,112.5
2301 | 2299,1940-08-31,175.9
2302 | 2300,1940-09-30,110.8
2303 | 2301,1940-10-31,91.5
2304 | 2302,1940-11-30,97.4
2305 | 2303,1940-12-31,113.8
2306 | 2304,1941-01-31,76.1
2307 | 2305,1941-02-28,74.3
2308 | 2306,1941-03-31,77.5
2309 | 2307,1941-04-30,54.6
2310 | 2308,1941-05-31,49.0
2311 | 2309,1941-06-30,99.6
2312 | 2310,1941-07-31,111.4
2313 | 2311,1941-08-31,100.1
2314 | 2312,1941-09-30,109.8
2315 | 2313,1941-10-31,77.2
2316 | 2314,1941-11-30,64.0
2317 | 2315,1941-12-31,56.2
2318 | 2316,1942-01-31,59.5
2319 | 2317,1942-02-28,87.9
2320 | 2318,1942-03-31,90.4
2321 | 2319,1942-04-30,101.2
2322 | 2320,1942-05-31,41.8
2323 | 2321,1942-06-30,18.9
2324 | 2322,1942-07-31,29.6
2325 | 2323,1942-08-31,33.7
2326 | 2324,1942-09-30,28.7
2327 | 2325,1942-10-31,32.0
2328 | 2326,1942-11-30,51.3
2329 | 2327,1942-12-31,37.6
2330 | 2328,1943-01-31,20.7
2331 | 2329,1943-02-28,48.1
2332 | 2330,1943-03-31,45.7
2333 | 2331,1943-04-30,43.5
2334 | 2332,1943-05-31,23.6
2335 | 2333,1943-06-30,12.7
2336 | 2334,1943-07-31,22.0
2337 | 2335,1943-08-31,32.3
2338 | 2336,1943-09-30,16.7
2339 | 2337,1943-10-31,13.0
2340 | 2338,1943-11-30,17.0
2341 | 2339,1943-12-31,31.4
2342 | 2340,1944-01-31,6.1
2343 | 2341,1944-02-29,0.8
2344 | 2342,1944-03-31,18.3
2345 | 2343,1944-04-30,0.4
2346 | 2344,1944-05-31,4.1
2347 | 2345,1944-06-30,8.2
2348 | 2346,1944-07-31,8.5
2349 | 2347,1944-08-31,27.9
2350 | 2348,1944-09-30,23.7
2351 | 2349,1944-10-31,28.1
2352 | 2350,1944-11-30,17.9
2353 | 2351,1944-12-31,47.4
2354 | 2352,1945-01-31,30.8
2355 | 2353,1945-02-28,21.2
2356 | 2354,1945-03-31,35.9
2357 | 2355,1945-04-30,53.4
2358 | 2356,1945-05-31,51.0
2359 | 2357,1945-06-30,60.2
2360 | 2358,1945-07-31,71.0
2361 | 2359,1945-08-31,43.1
2362 | 2360,1945-09-30,58.1
2363 | 2361,1945-10-31,114.5
2364 | 2362,1945-11-30,76.6
2365 | 2363,1945-12-31,45.7
2366 | 2364,1946-01-31,79.3
2367 | 2365,1946-02-28,143.8
2368 | 2366,1946-03-31,127.7
2369 | 2367,1946-04-30,126.1
2370 | 2368,1946-05-31,141.6
2371 | 2369,1946-06-30,122.6
2372 | 2370,1946-07-31,193.7
2373 | 2371,1946-08-31,178.7
2374 | 2372,1946-09-30,157.4
2375 | 2373,1946-10-31,170.5
2376 | 2374,1946-11-30,206.3
2377 | 2375,1946-12-31,202.9
2378 | 2376,1947-01-31,163.7
2379 | 2377,1947-02-28,188.9
2380 | 2378,1947-03-31,183.8
2381 | 2379,1947-04-30,212.1
2382 | 2380,1947-05-31,285.0
2383 | 2381,1947-06-30,232.1
2384 | 2382,1947-07-31,223.5
2385 | 2383,1947-08-31,267.4
2386 | 2384,1947-09-30,239.9
2387 | 2385,1947-10-31,231.7
2388 | 2386,1947-11-30,181.3
2389 | 2387,1947-12-31,164.9
2390 | 2388,1948-01-31,153.6
2391 | 2389,1948-02-29,122.0
2392 | 2390,1948-03-31,134.3
2393 | 2391,1948-04-30,268.5
2394 | 2392,1948-05-31,246.4
2395 | 2393,1948-06-30,237.5
2396 | 2394,1948-07-31,201.4
2397 | 2395,1948-08-31,223.7
2398 | 2396,1948-09-30,202.9
2399 | 2397,1948-10-31,192.9
2400 | 2398,1948-11-30,135.6
2401 | 2399,1948-12-31,195.3
2402 | 2400,1949-01-31,168.6
2403 | 2401,1949-02-28,258.0
2404 | 2402,1949-03-31,223.0
2405 | 2403,1949-04-30,208.1
2406 | 2404,1949-05-31,150.4
2407 | 2405,1949-06-30,172.4
2408 | 2406,1949-07-31,178.3
2409 | 2407,1949-08-31,175.3
2410 | 2408,1949-09-30,205.8
2411 | 2409,1949-10-31,186.3
2412 | 2410,1949-11-30,203.2
2413 | 2411,1949-12-31,166.6
2414 | 2412,1950-01-31,143.9
2415 | 2413,1950-02-28,134.3
2416 | 2414,1950-03-31,155.4
2417 | 2415,1950-04-30,160.6
2418 | 2416,1950-05-31,150.5
2419 | 2417,1950-06-30,118.3
2420 | 2418,1950-07-31,128.9
2421 | 2419,1950-08-31,120.6
2422 | 2420,1950-09-30,72.7
2423 | 2421,1950-10-31,87.0
2424 | 2422,1950-11-30,77.7
2425 | 2423,1950-12-31,76.7
2426 | 2424,1951-01-31,85.0
2427 | 2425,1951-02-28,84.8
2428 | 2426,1951-03-31,79.3
2429 | 2427,1951-04-30,131.7
2430 | 2428,1951-05-31,153.7
2431 | 2429,1951-06-30,142.4
2432 | 2430,1951-07-31,87.2
2433 | 2431,1951-08-31,86.5
2434 | 2432,1951-09-30,117.7
2435 | 2433,1951-10-31,73.3
2436 | 2434,1951-11-30,74.2
2437 | 2435,1951-12-31,65.0
2438 | 2436,1952-01-31,57.9
2439 | 2437,1952-02-29,32.4
2440 | 2438,1952-03-31,31.5
2441 | 2439,1952-04-30,41.7
2442 | 2440,1952-05-31,33.6
2443 | 2441,1952-06-30,52.0
2444 | 2442,1952-07-31,56.1
2445 | 2443,1952-08-31,77.8
2446 | 2444,1952-09-30,40.4
2447 | 2445,1952-10-31,34.2
2448 | 2446,1952-11-30,31.8
2449 | 2447,1952-12-31,48.9
2450 | 2448,1953-01-31,37.9
2451 | 2449,1953-02-28,5.9
2452 | 2450,1953-03-31,14.7
2453 | 2451,1953-04-30,39.6
2454 | 2452,1953-05-31,18.3
2455 | 2453,1953-06-30,31.5
2456 | 2454,1953-07-31,12.7
2457 | 2455,1953-08-31,33.6
2458 | 2456,1953-09-30,28.0
2459 | 2457,1953-10-31,12.3
2460 | 2458,1953-11-30,2.5
2461 | 2459,1953-12-31,3.9
2462 | 2460,1954-01-31,0.4
2463 | 2461,1954-02-28,0.8
2464 | 2462,1954-03-31,15.8
2465 | 2463,1954-04-30,2.7
2466 | 2464,1954-05-31,1.2
2467 | 2465,1954-06-30,0.4
2468 | 2466,1954-07-31,7.3
2469 | 2467,1954-08-31,12.6
2470 | 2468,1954-09-30,2.3
2471 | 2469,1954-10-31,10.5
2472 | 2470,1954-11-30,13.4
2473 | 2471,1954-12-31,11.3
2474 | 2472,1955-01-31,33.4
2475 | 2473,1955-02-28,29.9
2476 | 2474,1955-03-31,7.3
2477 | 2475,1955-04-30,16.4
2478 | 2476,1955-05-31,41.3
2479 | 2477,1955-06-30,45.2
2480 | 2478,1955-07-31,38.2
2481 | 2479,1955-08-31,58.0
2482 | 2480,1955-09-30,60.8
2483 | 2481,1955-10-31,83.0
2484 | 2482,1955-11-30,126.3
2485 | 2483,1955-12-31,108.8
2486 | 2484,1956-01-31,104.1
2487 | 2485,1956-02-29,175.6
2488 | 2486,1956-03-31,167.7
2489 | 2487,1956-04-30,156.7
2490 | 2488,1956-05-31,193.4
2491 | 2489,1956-06-30,165.1
2492 | 2490,1956-07-31,182.7
2493 | 2491,1956-08-31,240.2
2494 | 2492,1956-09-30,245.4
2495 | 2493,1956-10-31,219.9
2496 | 2494,1956-11-30,285.0
2497 | 2495,1956-12-31,272.0
2498 | 2496,1957-01-31,233.7
2499 | 2497,1957-02-28,184.5
2500 | 2498,1957-03-31,222.8
2501 | 2499,1957-04-30,248.0
2502 | 2500,1957-05-31,233.0
2503 | 2501,1957-06-30,284.3
2504 | 2502,1957-07-31,265.1
2505 | 2503,1957-08-31,223.7
2506 | 2504,1957-09-30,334.0
2507 | 2505,1957-10-31,359.4
2508 | 2506,1957-11-30,298.6
2509 | 2507,1957-12-31,339.0
2510 | 2508,1958-01-31,286.7
2511 | 2509,1958-02-28,233.6
2512 | 2510,1958-03-31,270.0
2513 | 2511,1958-04-30,277.6
2514 | 2512,1958-05-31,248.2
2515 | 2513,1958-06-30,242.9
2516 | 2514,1958-07-31,271.0
2517 | 2515,1958-08-31,283.5
2518 | 2516,1958-09-30,285.1
2519 | 2517,1958-10-31,256.9
2520 | 2518,1958-11-30,215.6
2521 | 2519,1958-12-31,265.7
2522 | 2520,1959-01-31,307.7
2523 | 2521,1959-02-28,202.6
2524 | 2522,1959-03-31,263.0
2525 | 2523,1959-04-30,231.3
2526 | 2524,1959-05-31,243.6
2527 | 2525,1959-06-30,238.9
2528 | 2526,1959-07-31,211.9
2529 | 2527,1959-08-31,282.6
2530 | 2528,1959-09-30,205.6
2531 | 2529,1959-10-31,157.7
2532 | 2530,1959-11-30,175.6
2533 | 2531,1959-12-31,177.1
2534 | 2532,1960-01-31,207.2
2535 | 2533,1960-02-29,149.9
2536 | 2534,1960-03-31,144.6
2537 | 2535,1960-04-30,172.7
2538 | 2536,1960-05-31,169.3
2539 | 2537,1960-06-30,156.0
2540 | 2538,1960-07-31,172.4
2541 | 2539,1960-08-31,190.0
2542 | 2540,1960-09-30,180.1
2543 | 2541,1960-10-31,117.3
2544 | 2542,1960-11-30,126.9
2545 | 2543,1960-12-31,121.2
2546 | 2544,1961-01-31,82.1
2547 | 2545,1961-02-28,65.4
2548 | 2546,1961-03-31,75.2
2549 | 2547,1961-04-30,86.9
2550 | 2548,1961-05-31,72.3
2551 | 2549,1961-06-30,109.5
2552 | 2550,1961-07-31,99.3
2553 | 2551,1961-08-31,79.2
2554 | 2552,1961-09-30,90.1
2555 | 2553,1961-10-31,53.7
2556 | 2554,1961-11-30,46.5
2557 | 2555,1961-12-31,56.9
2558 | 2556,1962-01-31,55.1
2559 | 2557,1962-02-28,71.7
2560 | 2558,1962-03-31,64.9
2561 | 2559,1962-04-30,65.9
2562 | 2560,1962-05-31,61.9
2563 | 2561,1962-06-30,59.6
2564 | 2562,1962-07-31,31.4
2565 | 2563,1962-08-31,31.5
2566 | 2564,1962-09-30,72.7
2567 | 2565,1962-10-31,56.1
2568 | 2566,1962-11-30,38.8
2569 | 2567,1962-12-31,33.2
2570 | 2568,1963-01-31,28.7
2571 | 2569,1963-02-28,35.2
2572 | 2570,1963-03-31,24.8
2573 | 2571,1963-04-30,41.7
2574 | 2572,1963-05-31,61.1
2575 | 2573,1963-06-30,51.2
2576 | 2574,1963-07-31,28.7
2577 | 2575,1963-08-31,47.5
2578 | 2576,1963-09-30,55.2
2579 | 2577,1963-10-31,50.1
2580 | 2578,1963-11-30,33.7
2581 | 2579,1963-12-31,21.6
2582 | 2580,1964-01-31,22.6
2583 | 2581,1964-02-29,25.3
2584 | 2582,1964-03-31,24.1
2585 | 2583,1964-04-30,12.9
2586 | 2584,1964-05-31,14.3
2587 | 2585,1964-06-30,13.5
2588 | 2586,1964-07-31,4.8
2589 | 2587,1964-08-31,13.8
2590 | 2588,1964-09-30,7.0
2591 | 2589,1964-10-31,9.2
2592 | 2590,1964-11-30,11.1
2593 | 2591,1964-12-31,22.1
2594 | 2592,1965-01-31,25.4
2595 | 2593,1965-02-28,20.8
2596 | 2594,1965-03-31,17.5
2597 | 2595,1965-04-30,10.2
2598 | 2596,1965-05-31,34.5
2599 | 2597,1965-06-30,23.3
2600 | 2598,1965-07-31,17.3
2601 | 2599,1965-08-31,13.3
2602 | 2600,1965-09-30,24.5
2603 | 2601,1965-10-31,29.1
2604 | 2602,1965-11-30,22.8
2605 | 2603,1965-12-31,24.7
2606 | 2604,1966-01-31,40.3
2607 | 2605,1966-02-28,35.3
2608 | 2606,1966-03-31,36.4
2609 | 2607,1966-04-30,69.0
2610 | 2608,1966-05-31,64.2
2611 | 2609,1966-06-30,67.7
2612 | 2610,1966-07-31,80.2
2613 | 2611,1966-08-31,72.6
2614 | 2612,1966-09-30,71.1
2615 | 2613,1966-10-31,81.2
2616 | 2614,1966-11-30,81.0
2617 | 2615,1966-12-31,99.8
2618 | 2616,1967-01-31,157.0
2619 | 2617,1967-02-28,132.6
2620 | 2618,1967-03-31,158.3
2621 | 2619,1967-04-30,98.4
2622 | 2620,1967-05-31,122.5
2623 | 2621,1967-06-30,95.4
2624 | 2622,1967-07-31,129.5
2625 | 2623,1967-08-31,151.8
2626 | 2624,1967-09-30,108.7
2627 | 2625,1967-10-31,125.0
2628 | 2626,1967-11-30,133.6
2629 | 2627,1967-12-31,179.0
2630 | 2628,1968-01-31,172.5
2631 | 2629,1968-02-29,158.5
2632 | 2630,1968-03-31,130.5
2633 | 2631,1968-04-30,115.0
2634 | 2632,1968-05-31,180.0
2635 | 2633,1968-06-30,156.2
2636 | 2634,1968-07-31,136.2
2637 | 2635,1968-08-31,154.8
2638 | 2636,1968-09-30,166.0
2639 | 2637,1968-10-31,152.5
2640 | 2638,1968-11-30,121.7
2641 | 2639,1968-12-31,155.5
2642 | 2640,1969-01-31,147.8
2643 | 2641,1969-02-28,170.5
2644 | 2642,1969-03-31,192.3
2645 | 2643,1969-04-30,151.1
2646 | 2644,1969-05-31,169.9
2647 | 2645,1969-06-30,150.1
2648 | 2646,1969-07-31,137.1
2649 | 2647,1969-08-31,138.8
2650 | 2648,1969-09-30,129.3
2651 | 2649,1969-10-31,135.4
2652 | 2650,1969-11-30,132.4
2653 | 2651,1969-12-31,138.6
2654 | 2652,1970-01-31,157.9
2655 | 2653,1970-02-28,180.8
2656 | 2654,1970-03-31,145.7
2657 | 2655,1970-04-30,155.1
2658 | 2656,1970-05-31,180.5
2659 | 2657,1970-06-30,151.3
2660 | 2658,1970-07-31,159.3
2661 | 2659,1970-08-31,131.7
2662 | 2660,1970-09-30,140.8
2663 | 2661,1970-10-31,122.6
2664 | 2662,1970-11-30,134.8
2665 | 2663,1970-12-31,118.2
2666 | 2664,1971-01-31,129.2
2667 | 2665,1971-02-28,111.8
2668 | 2666,1971-03-31,85.9
2669 | 2667,1971-04-30,101.6
2670 | 2668,1971-05-31,81.5
2671 | 2669,1971-06-30,70.7
2672 | 2670,1971-07-31,114.7
2673 | 2671,1971-08-31,87.0
2674 | 2672,1971-09-30,71.3
2675 | 2673,1971-10-31,73.4
2676 | 2674,1971-11-30,89.5
2677 | 2675,1971-12-31,116.5
2678 | 2676,1972-01-31,87.0
2679 | 2677,1972-02-29,125.3
2680 | 2678,1972-03-31,113.5
2681 | 2679,1972-04-30,89.6
2682 | 2680,1972-05-31,113.9
2683 | 2681,1972-06-30,124.7
2684 | 2682,1972-07-31,108.3
2685 | 2683,1972-08-31,108.9
2686 | 2684,1972-09-30,90.7
2687 | 2685,1972-10-31,86.9
2688 | 2686,1972-11-30,59.2
2689 | 2687,1972-12-31,64.3
2690 | 2688,1973-01-31,61.8
2691 | 2689,1973-02-28,60.9
2692 | 2690,1973-03-31,65.4
2693 | 2691,1973-04-30,81.8
2694 | 2692,1973-05-31,60.3
2695 | 2693,1973-06-30,56.1
2696 | 2694,1973-07-31,33.2
2697 | 2695,1973-08-31,36.6
2698 | 2696,1973-09-30,84.1
2699 | 2697,1973-10-31,43.7
2700 | 2698,1973-11-30,34.3
2701 | 2699,1973-12-31,33.3
2702 | 2700,1974-01-31,39.4
2703 | 2701,1974-02-28,37.3
2704 | 2702,1974-03-31,30.9
2705 | 2703,1974-04-30,57.5
2706 | 2704,1974-05-31,56.3
2707 | 2705,1974-06-30,51.5
2708 | 2706,1974-07-31,79.1
2709 | 2707,1974-08-31,47.9
2710 | 2708,1974-09-30,57.2
2711 | 2709,1974-10-31,67.2
2712 | 2710,1974-11-30,35.9
2713 | 2711,1974-12-31,29.6
2714 | 2712,1975-01-31,27.3
2715 | 2713,1975-02-28,16.7
2716 | 2714,1975-03-31,16.9
2717 | 2715,1975-04-30,7.7
2718 | 2716,1975-05-31,13.1
2719 | 2717,1975-06-30,16.7
2720 | 2718,1975-07-31,40.4
2721 | 2719,1975-08-31,56.7
2722 | 2720,1975-09-30,20.3
2723 | 2721,1975-10-31,13.6
2724 | 2722,1975-11-30,27.9
2725 | 2723,1975-12-31,11.6
2726 | 2724,1976-01-31,11.9
2727 | 2725,1976-02-29,6.4
2728 | 2726,1976-03-31,31.5
2729 | 2727,1976-04-30,27.3
2730 | 2728,1976-05-31,18.2
2731 | 2729,1976-06-30,17.9
2732 | 2730,1976-07-31,2.9
2733 | 2731,1976-08-31,24.1
2734 | 2732,1976-09-30,20.0
2735 | 2733,1976-10-31,29.7
2736 | 2734,1976-11-30,7.9
2737 | 2735,1976-12-31,22.3
2738 | 2736,1977-01-31,23.8
2739 | 2737,1977-02-28,33.3
2740 | 2738,1977-03-31,13.0
2741 | 2739,1977-04-30,19.0
2742 | 2740,1977-05-31,27.0
2743 | 2741,1977-06-30,54.9
2744 | 2742,1977-07-31,30.6
2745 | 2743,1977-08-31,43.0
2746 | 2744,1977-09-30,62.4
2747 | 2745,1977-10-31,62.1
2748 | 2746,1977-11-30,41.6
2749 | 2747,1977-12-31,61.4
2750 | 2748,1978-01-31,73.7
2751 | 2749,1978-02-28,132.6
2752 | 2750,1978-03-31,108.4
2753 | 2751,1978-04-30,141.2
2754 | 2752,1978-05-31,117.1
2755 | 2753,1978-06-30,134.6
2756 | 2754,1978-07-31,99.7
2757 | 2755,1978-08-31,82.4
2758 | 2756,1978-09-30,195.7
2759 | 2757,1978-10-31,177.1
2760 | 2758,1978-11-30,138.5
2761 | 2759,1978-12-31,173.9
2762 | 2760,1979-01-31,235.9
2763 | 2761,1979-02-28,194.7
2764 | 2762,1979-03-31,195.3
2765 | 2763,1979-04-30,143.7
2766 | 2764,1979-05-31,190.3
2767 | 2765,1979-06-30,211.7
2768 | 2766,1979-07-31,225.7
2769 | 2767,1979-08-31,201.4
2770 | 2768,1979-09-30,266.9
2771 | 2769,1979-10-31,263.6
2772 | 2770,1979-11-30,259.5
2773 | 2771,1979-12-31,249.6
2774 | 2772,1980-01-31,226.1
2775 | 2773,1980-02-29,219.4
2776 | 2774,1980-03-31,178.7
2777 | 2775,1980-04-30,232.2
2778 | 2776,1980-05-31,254.7
2779 | 2777,1980-06-30,222.7
2780 | 2778,1980-07-31,192.9
2781 | 2779,1980-08-31,191.7
2782 | 2780,1980-09-30,219.6
2783 | 2781,1980-10-31,233.3
2784 | 2782,1980-11-30,209.5
2785 | 2783,1980-12-31,246.9
2786 | 2784,1981-01-31,156.6
2787 | 2785,1981-02-28,189.9
2788 | 2786,1981-03-31,196.6
2789 | 2787,1981-04-30,225.3
2790 | 2788,1981-05-31,194.7
2791 | 2789,1981-06-30,131.6
2792 | 2790,1981-07-31,205.3
2793 | 2791,1981-08-31,242.5
2794 | 2792,1981-09-30,245.3
2795 | 2793,1981-10-31,216.2
2796 | 2794,1981-11-30,186.0
2797 | 2795,1981-12-31,195.4
2798 | 2796,1982-01-31,149.8
2799 | 2797,1982-02-28,230.9
2800 | 2798,1982-03-31,221.1
2801 | 2799,1982-04-30,170.3
2802 | 2800,1982-05-31,119.3
2803 | 2801,1982-06-30,163.7
2804 | 2802,1982-07-31,139.4
2805 | 2803,1982-08-31,161.9
2806 | 2804,1982-09-30,167.4
2807 | 2805,1982-10-31,134.3
2808 | 2806,1982-11-30,127.5
2809 | 2807,1982-12-31,169.0
2810 | 2808,1983-01-31,115.5
2811 | 2809,1983-02-28,73.1
2812 | 2810,1983-03-31,88.7
2813 | 2811,1983-04-30,109.6
2814 | 2812,1983-05-31,132.5
2815 | 2813,1983-06-30,131.5
2816 | 2814,1983-07-31,108.9
2817 | 2815,1983-08-31,96.0
2818 | 2816,1983-09-30,69.9
2819 | 2817,1983-10-31,72.5
2820 | 2818,1983-11-30,45.7
2821 | 2819,1983-12-31,45.6
2822 | 2820,1984-01-31,74.8
2823 | 2821,1984-02-29,110.2
2824 | 2822,1984-03-31,116.7
2825 | 2823,1984-04-30,90.4
2826 | 2824,1984-05-31,96.9
2827 | 2825,1984-06-30,65.1
2828 | 2826,1984-07-31,55.7
2829 | 2827,1984-08-31,35.0
2830 | 2828,1984-09-30,22.6
2831 | 2829,1984-10-31,12.6
2832 | 2830,1984-11-30,26.5
2833 | 2831,1984-12-31,21.4
2834 | 2832,1985-01-31,17.8
2835 | 2833,1985-02-28,20.7
2836 | 2834,1985-03-31,16.9
2837 | 2835,1985-04-30,20.4
2838 | 2836,1985-05-31,32.4
2839 | 2837,1985-06-30,28.3
2840 | 2838,1985-07-31,39.9
2841 | 2839,1985-08-31,10.1
2842 | 2840,1985-09-30,4.3
2843 | 2841,1985-10-31,22.0
2844 | 2842,1985-11-30,17.9
2845 | 2843,1985-12-31,15.8
2846 | 2844,1986-01-31,2.8
2847 | 2845,1986-02-28,27.9
2848 | 2846,1986-03-31,13.8
2849 | 2847,1986-04-30,22.4
2850 | 2848,1986-05-31,16.1
2851 | 2849,1986-06-30,0.6
2852 | 2850,1986-07-31,18.1
2853 | 2851,1986-08-31,9.9
2854 | 2852,1986-09-30,5.1
2855 | 2853,1986-10-31,40.1
2856 | 2854,1986-11-30,15.4
2857 | 2855,1986-12-31,5.8
2858 | 2856,1987-01-31,9.8
2859 | 2857,1987-02-28,3.4
2860 | 2858,1987-03-31,17.4
2861 | 2859,1987-04-30,46.0
2862 | 2860,1987-05-31,39.1
2863 | 2861,1987-06-30,18.8
2864 | 2862,1987-07-31,38.2
2865 | 2863,1987-08-31,47.9
2866 | 2864,1987-09-30,42.2
2867 | 2865,1987-10-31,63.4
2868 | 2866,1987-11-30,48.8
2869 | 2867,1987-12-31,29.1
2870 | 2868,1988-01-31,70.5
2871 | 2869,1988-02-29,45.4
2872 | 2870,1988-03-31,91.2
2873 | 2871,1988-04-30,108.8
2874 | 2872,1988-05-31,74.2
2875 | 2873,1988-06-30,124.3
2876 | 2874,1988-07-31,131.4
2877 | 2875,1988-08-31,139.4
2878 | 2876,1988-09-30,142.7
2879 | 2877,1988-10-31,156.5
2880 | 2878,1988-11-30,156.8
2881 | 2879,1988-12-31,231.2
2882 | 2880,1989-01-31,210.1
2883 | 2881,1989-02-28,208.7
2884 | 2882,1989-03-31,170.4
2885 | 2883,1989-04-30,166.3
2886 | 2884,1989-05-31,195.4
2887 | 2885,1989-06-30,284.5
2888 | 2886,1989-07-31,180.5
2889 | 2887,1989-08-31,232.0
2890 | 2888,1989-09-30,225.1
2891 | 2889,1989-10-31,212.2
2892 | 2890,1989-11-30,238.2
2893 | 2891,1989-12-31,211.4
2894 | 2892,1990-01-31,227.4
2895 | 2893,1990-02-28,171.8
2896 | 2894,1990-03-31,191.7
2897 | 2895,1990-04-30,189.7
2898 | 2896,1990-05-31,175.2
2899 | 2897,1990-06-30,153.3
2900 | 2898,1990-07-31,191.1
2901 | 2899,1990-08-31,252.1
2902 | 2900,1990-09-30,169.1
2903 | 2901,1990-10-31,199.4
2904 | 2902,1990-11-30,178.8
2905 | 2903,1990-12-31,197.1
2906 | 2904,1991-01-31,195.3
2907 | 2905,1991-02-28,240.3
2908 | 2906,1991-03-31,197.0
2909 | 2907,1991-04-30,197.6
2910 | 2908,1991-05-31,166.9
2911 | 2909,1991-06-30,224.7
2912 | 2910,1991-07-31,240.2
2913 | 2911,1991-08-31,240.8
2914 | 2912,1991-09-30,168.9
2915 | 2913,1991-10-31,197.1
2916 | 2914,1991-11-30,159.5
2917 | 2915,1991-12-31,212.6
2918 | 2916,1992-01-31,198.3
2919 | 2917,1992-02-29,230.7
2920 | 2918,1992-03-31,151.0
2921 | 2919,1992-04-30,142.2
2922 | 2920,1992-05-31,94.3
2923 | 2921,1992-06-30,98.5
2924 | 2922,1992-07-31,114.2
2925 | 2923,1992-08-31,91.9
2926 | 2924,1992-09-30,94.0
2927 | 2925,1992-10-31,133.4
2928 | 2926,1992-11-30,129.6
2929 | 2927,1992-12-31,122.0
2930 | 2928,1993-01-31,81.4
2931 | 2929,1993-02-28,127.8
2932 | 2930,1993-03-31,102.4
2933 | 2931,1993-04-30,94.4
2934 | 2932,1993-05-31,78.8
2935 | 2933,1993-06-30,69.6
2936 | 2934,1993-07-31,80.4
2937 | 2935,1993-08-31,62.5
2938 | 2936,1993-09-30,31.2
2939 | 2937,1993-10-31,71.1
2940 | 2938,1993-11-30,48.2
2941 | 2939,1993-12-31,68.4
2942 | 2940,1994-01-31,84.9
2943 | 2941,1994-02-28,54.9
2944 | 2942,1994-03-31,47.5
2945 | 2943,1994-04-30,27.4
2946 | 2944,1994-05-31,29.8
2947 | 2945,1994-06-30,39.7
2948 | 2946,1994-07-31,50.6
2949 | 2947,1994-08-31,34.3
2950 | 2948,1994-09-30,40.5
2951 | 2949,1994-10-31,67.1
2952 | 2950,1994-11-30,29.5
2953 | 2951,1994-12-31,32.2
2954 | 2952,1995-01-31,32.6
2955 | 2953,1995-02-28,45.8
2956 | 2954,1995-03-31,46.3
2957 | 2955,1995-04-30,21.6
2958 | 2956,1995-05-31,19.4
2959 | 2957,1995-06-30,22.5
2960 | 2958,1995-07-31,20.4
2961 | 2959,1995-08-31,18.2
2962 | 2960,1995-09-30,15.7
2963 | 2961,1995-10-31,30.6
2964 | 2962,1995-11-30,14.0
2965 | 2963,1995-12-31,14.9
2966 | 2964,1996-01-31,13.3
2967 | 2965,1996-02-29,7.7
2968 | 2966,1996-03-31,12.6
2969 | 2967,1996-04-30,6.8
2970 | 2968,1996-05-31,7.6
2971 | 2969,1996-06-30,16.5
2972 | 2970,1996-07-31,11.8
2973 | 2971,1996-08-31,19.7
2974 | 2972,1996-09-30,3.0
2975 | 2973,1996-10-31,0.7
2976 | 2974,1996-11-30,24.9
2977 | 2975,1996-12-31,14.0
2978 | 2976,1997-01-31,7.4
2979 | 2977,1997-02-28,11.0
2980 | 2978,1997-03-31,12.1
2981 | 2979,1997-04-30,23.0
2982 | 2980,1997-05-31,25.4
2983 | 2981,1997-06-30,20.8
2984 | 2982,1997-07-31,12.9
2985 | 2983,1997-08-31,35.7
2986 | 2984,1997-09-30,59.7
2987 | 2985,1997-10-31,32.8
2988 | 2986,1997-11-30,50.4
2989 | 2987,1997-12-31,55.5
2990 | 2988,1998-01-31,44.5
2991 | 2989,1998-02-28,50.2
2992 | 2990,1998-03-31,82.0
2993 | 2991,1998-04-30,70.6
2994 | 2992,1998-05-31,74.0
2995 | 2993,1998-06-30,90.5
2996 | 2994,1998-07-31,96.7
2997 | 2995,1998-08-31,121.1
2998 | 2996,1998-09-30,132.0
2999 | 2997,1998-10-31,78.5
3000 | 2998,1998-11-30,97.3
3001 | 2999,1998-12-31,119.2
3002 | 3000,1999-01-31,86.0
3003 | 3001,1999-02-28,98.0
3004 | 3002,1999-03-31,103.5
3005 | 3003,1999-04-30,93.6
3006 | 3004,1999-05-31,149.6
3007 | 3005,1999-06-30,207.2
3008 | 3006,1999-07-31,173.5
3009 | 3007,1999-08-31,142.3
3010 | 3008,1999-09-30,106.3
3011 | 3009,1999-10-31,168.7
3012 | 3010,1999-11-30,188.3
3013 | 3011,1999-12-31,116.8
3014 | 3012,2000-01-31,133.1
3015 | 3013,2000-02-29,165.7
3016 | 3014,2000-03-31,217.7
3017 | 3015,2000-04-30,191.5
3018 | 3016,2000-05-31,165.9
3019 | 3017,2000-06-30,188.0
3020 | 3018,2000-07-31,244.3
3021 | 3019,2000-08-31,180.5
3022 | 3020,2000-09-30,156.0
3023 | 3021,2000-10-31,141.6
3024 | 3022,2000-11-30,158.1
3025 | 3023,2000-12-31,143.3
3026 | 3024,2001-01-31,142.6
3027 | 3025,2001-02-28,121.5
3028 | 3026,2001-03-31,165.8
3029 | 3027,2001-04-30,161.7
3030 | 3028,2001-05-31,142.1
3031 | 3029,2001-06-30,202.9
3032 | 3030,2001-07-31,123.0
3033 | 3031,2001-08-31,161.5
3034 | 3032,2001-09-30,238.2
3035 | 3033,2001-10-31,194.1
3036 | 3034,2001-11-30,176.6
3037 | 3035,2001-12-31,213.4
3038 | 3036,2002-01-31,184.6
3039 | 3037,2002-02-28,170.2
3040 | 3038,2002-03-31,147.1
3041 | 3039,2002-04-30,186.9
3042 | 3040,2002-05-31,187.5
3043 | 3041,2002-06-30,128.8
3044 | 3042,2002-07-31,161.0
3045 | 3043,2002-08-31,175.6
3046 | 3044,2002-09-30,187.9
3047 | 3045,2002-10-31,151.2
3048 | 3046,2002-11-30,147.2
3049 | 3047,2002-12-31,135.3
3050 | 3048,2003-01-31,133.5
3051 | 3049,2003-02-28,75.7
3052 | 3050,2003-03-31,100.7
3053 | 3051,2003-04-30,97.9
3054 | 3052,2003-05-31,86.8
3055 | 3053,2003-06-30,118.7
3056 | 3054,2003-07-31,128.3
3057 | 3055,2003-08-31,115.4
3058 | 3056,2003-09-30,78.5
3059 | 3057,2003-10-31,97.8
3060 | 3058,2003-11-30,82.9
3061 | 3059,2003-12-31,72.2
3062 | 3060,2004-01-31,60.6
3063 | 3061,2004-02-29,74.6
3064 | 3062,2004-03-31,74.8
3065 | 3063,2004-04-30,59.2
3066 | 3064,2004-05-31,72.8
3067 | 3065,2004-06-30,66.5
3068 | 3066,2004-07-31,83.8
3069 | 3067,2004-08-31,69.7
3070 | 3068,2004-09-30,48.8
3071 | 3069,2004-10-31,74.2
3072 | 3070,2004-11-30,70.1
3073 | 3071,2004-12-31,28.9
3074 | 3072,2005-01-31,48.1
3075 | 3073,2005-02-28,43.5
3076 | 3074,2005-03-31,39.6
3077 | 3075,2005-04-30,38.7
3078 | 3076,2005-05-31,61.9
3079 | 3077,2005-06-30,56.8
3080 | 3078,2005-07-31,62.4
3081 | 3079,2005-08-31,60.5
3082 | 3080,2005-09-30,37.2
3083 | 3081,2005-10-31,13.2
3084 | 3082,2005-11-30,27.5
3085 | 3083,2005-12-31,59.3
3086 | 3084,2006-01-31,20.9
3087 | 3085,2006-02-28,5.7
3088 | 3086,2006-03-31,17.3
3089 | 3087,2006-04-30,50.3
3090 | 3088,2006-05-31,37.2
3091 | 3089,2006-06-30,24.5
3092 | 3090,2006-07-31,22.2
3093 | 3091,2006-08-31,20.8
3094 | 3092,2006-09-30,23.7
3095 | 3093,2006-10-31,14.9
3096 | 3094,2006-11-30,35.7
3097 | 3095,2006-12-31,22.3
3098 | 3096,2007-01-31,29.3
3099 | 3097,2007-02-28,18.4
3100 | 3098,2007-03-31,7.2
3101 | 3099,2007-04-30,5.4
3102 | 3100,2007-05-31,19.5
3103 | 3101,2007-06-30,21.3
3104 | 3102,2007-07-31,15.1
3105 | 3103,2007-08-31,9.8
3106 | 3104,2007-09-30,4.0
3107 | 3105,2007-10-31,1.5
3108 | 3106,2007-11-30,2.8
3109 | 3107,2007-12-31,17.3
3110 | 3108,2008-01-31,4.1
3111 | 3109,2008-02-29,2.9
3112 | 3110,2008-03-31,15.5
3113 | 3111,2008-04-30,3.6
3114 | 3112,2008-05-31,4.6
3115 | 3113,2008-06-30,5.2
3116 | 3114,2008-07-31,0.6
3117 | 3115,2008-08-31,0.3
3118 | 3116,2008-09-30,1.2
3119 | 3117,2008-10-31,4.2
3120 | 3118,2008-11-30,6.6
3121 | 3119,2008-12-31,1.0
3122 | 3120,2009-01-31,1.3
3123 | 3121,2009-02-28,1.2
3124 | 3122,2009-03-31,0.6
3125 | 3123,2009-04-30,1.2
3126 | 3124,2009-05-31,2.9
3127 | 3125,2009-06-30,6.3
3128 | 3126,2009-07-31,5.5
3129 | 3127,2009-08-31,0.0
3130 | 3128,2009-09-30,7.1
3131 | 3129,2009-10-31,7.7
3132 | 3130,2009-11-30,6.9
3133 | 3131,2009-12-31,16.3
3134 | 3132,2010-01-31,19.5
3135 | 3133,2010-02-28,28.5
3136 | 3134,2010-03-31,24.0
3137 | 3135,2010-04-30,10.4
3138 | 3136,2010-05-31,13.9
3139 | 3137,2010-06-30,18.8
3140 | 3138,2010-07-31,25.2
3141 | 3139,2010-08-31,29.6
3142 | 3140,2010-09-30,36.4
3143 | 3141,2010-10-31,33.6
3144 | 3142,2010-11-30,34.4
3145 | 3143,2010-12-31,24.5
3146 | 3144,2011-01-31,27.3
3147 | 3145,2011-02-28,48.3
3148 | 3146,2011-03-31,78.6
3149 | 3147,2011-04-30,76.1
3150 | 3148,2011-05-31,58.2
3151 | 3149,2011-06-30,56.1
3152 | 3150,2011-07-31,64.5
3153 | 3151,2011-08-31,65.8
3154 | 3152,2011-09-30,120.1
3155 | 3153,2011-10-31,125.7
3156 | 3154,2011-11-30,139.1
3157 | 3155,2011-12-31,109.3
3158 | 3156,2012-01-31,94.4
3159 | 3157,2012-02-29,47.8
3160 | 3158,2012-03-31,86.6
3161 | 3159,2012-04-30,85.9
3162 | 3160,2012-05-31,96.5
3163 | 3161,2012-06-30,92.0
3164 | 3162,2012-07-31,100.1
3165 | 3163,2012-08-31,94.8
3166 | 3164,2012-09-30,93.7
3167 | 3165,2012-10-31,76.5
3168 | 3166,2012-11-30,87.6
3169 | 3167,2012-12-31,56.8
3170 | 3168,2013-01-31,96.1
3171 | 3169,2013-02-28,60.9
3172 | 3170,2013-03-31,78.3
3173 | 3171,2013-04-30,107.3
3174 | 3172,2013-05-31,120.2
3175 | 3173,2013-06-30,76.7
3176 | 3174,2013-07-31,86.2
3177 | 3175,2013-08-31,91.8
3178 | 3176,2013-09-30,54.5
3179 | 3177,2013-10-31,114.4
3180 | 3178,2013-11-30,113.9
3181 | 3179,2013-12-31,124.2
3182 | 3180,2014-01-31,117.0
3183 | 3181,2014-02-28,146.1
3184 | 3182,2014-03-31,128.7
3185 | 3183,2014-04-30,112.5
3186 | 3184,2014-05-31,112.5
3187 | 3185,2014-06-30,102.9
3188 | 3186,2014-07-31,100.2
3189 | 3187,2014-08-31,106.9
3190 | 3188,2014-09-30,130.0
3191 | 3189,2014-10-31,90.0
3192 | 3190,2014-11-30,103.6
3193 | 3191,2014-12-31,112.9
3194 | 3192,2015-01-31,93.0
3195 | 3193,2015-02-28,66.7
3196 | 3194,2015-03-31,54.5
3197 | 3195,2015-04-30,75.3
3198 | 3196,2015-05-31,88.8
3199 | 3197,2015-06-30,66.5
3200 | 3198,2015-07-31,65.8
3201 | 3199,2015-08-31,64.4
3202 | 3200,2015-09-30,78.6
3203 | 3201,2015-10-31,63.6
3204 | 3202,2015-11-30,62.2
3205 | 3203,2015-12-31,58.0
3206 | 3204,2016-01-31,57.0
3207 | 3205,2016-02-29,56.4
3208 | 3206,2016-03-31,54.1
3209 | 3207,2016-04-30,37.9
3210 | 3208,2016-05-31,51.5
3211 | 3209,2016-06-30,20.5
3212 | 3210,2016-07-31,32.4
3213 | 3211,2016-08-31,50.2
3214 | 3212,2016-09-30,44.6
3215 | 3213,2016-10-31,33.4
3216 | 3214,2016-11-30,21.4
3217 | 3215,2016-12-31,18.5
3218 | 3216,2017-01-31,26.1
3219 | 3217,2017-02-28,26.4
3220 | 3218,2017-03-31,17.7
3221 | 3219,2017-04-30,32.3
3222 | 3220,2017-05-31,18.9
3223 | 3221,2017-06-30,19.2
3224 | 3222,2017-07-31,17.8
3225 | 3223,2017-08-31,32.6
3226 | 3224,2017-09-30,43.7
3227 | 3225,2017-10-31,13.2
3228 | 3226,2017-11-30,5.7
3229 | 3227,2017-12-31,8.2
3230 | 3228,2018-01-31,6.8
3231 | 3229,2018-02-28,10.7
3232 | 3230,2018-03-31,2.5
3233 | 3231,2018-04-30,8.9
3234 | 3232,2018-05-31,13.2
3235 | 3233,2018-06-30,15.9
3236 | 3234,2018-07-31,1.6
3237 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 Aditya Bhattacharya
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # TimeSeries-Using-TensorFlow
2 | 
3 | ![Time Series Forecasting with TensorFlow](https://i0.wp.com/aditya-bhattacharya.net/wp-content/uploads/2020/12/image.png)
4 | 
5 | Visit my website for a detailed explanation and code walkthrough. [Time Series Forecasting using Deep Learning with TensorFlow](https://aditya-bhattacharya.net/2020/07/11/time-series-tips-and-tricks/2/)
6 | 
7 | Read the medium post for [Time Series Forecasting using TensorFlow and Deep Hybrid Learning](https://adib0073.medium.com/time-series-forecasting-using-tensorflow-and-deep-hybrid-learning-5088ad76dd55)
8 | 
9 | 


--------------------------------------------------------------------------------
/SampleCode/Readme.md:
--------------------------------------------------------------------------------
1 | Referred notebooks from https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction/
2 | 


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_1_Lesson_3_Notebook.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "accelerator": "GPU",
  6 |     "colab": {
  7 |       "name": "S+P Week 1 - Lesson 3 - Notebook.ipynb",
  8 |       "provenance": [],
  9 |       "collapsed_sections": [],
 10 |       "toc_visible": true
 11 |     },
 12 |     "kernelspec": {
 13 |       "display_name": "Python 3",
 14 |       "name": "python3"
 15 |     }
 16 |   },
 17 |   "cells": [
 18 |     {
 19 |       "cell_type": "code",
 20 |       "metadata": {
 21 |         "id": "zX4Kg8DUTKWO"
 22 |       },
 23 |       "source": [
 24 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 25 |         "# you may not use this file except in compliance with the License.\n",
 26 |         "# You may obtain a copy of the License at\n",
 27 |         "#\n",
 28 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 29 |         "#\n",
 30 |         "# Unless required by applicable law or agreed to in writing, software\n",
 31 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 32 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 33 |         "# See the License for the specific language governing permissions and\n",
 34 |         "# limitations under the License."
 35 |       ],
 36 |       "execution_count": null,
 37 |       "outputs": []
 38 |     },
 39 |     {
 40 |       "cell_type": "markdown",
 41 |       "metadata": {
 42 |         "id": "view-in-github"
 43 |       },
 44 |       "source": [
 45 |         "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%201%20-%20Lesson%203%20-%20Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 46 |       ]
 47 |     },
 48 |     {
 49 |       "cell_type": "code",
 50 |       "metadata": {
 51 |         "id": "y7QztBIVR1tb"
 52 |       },
 53 |       "source": [
 54 |         "try:\n",
 55 |         "  # %tensorflow_version only exists in Colab.\n",
 56 |         "  %tensorflow_version 2.x\n",
 57 |         "except Exception:\n",
 58 |         "  pass\n"
 59 |       ],
 60 |       "execution_count": null,
 61 |       "outputs": []
 62 |     },
 63 |     {
 64 |       "cell_type": "code",
 65 |       "metadata": {
 66 |         "id": "t9HrvPfrSlzS"
 67 |       },
 68 |       "source": [
 69 |         "import tensorflow as tf\n",
 70 |         "print(tf.__version__)\n"
 71 |       ],
 72 |       "execution_count": null,
 73 |       "outputs": []
 74 |     },
 75 |     {
 76 |       "cell_type": "markdown",
 77 |       "metadata": {
 78 |         "id": "I9x4GjlEVTQN"
 79 |       },
 80 |       "source": [
 81 |         "The next code block will set up the time series with seasonality, trend and a bit of noise. "
 82 |       ]
 83 |     },
 84 |     {
 85 |       "cell_type": "code",
 86 |       "metadata": {
 87 |         "id": "gqWabzlJ63nL"
 88 |       },
 89 |       "source": [
 90 |         "import numpy as np\n",
 91 |         "import matplotlib.pyplot as plt\n",
 92 |         "import tensorflow as tf\n",
 93 |         "from tensorflow import keras\n",
 94 |         "\n",
 95 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 96 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 97 |         "    plt.xlabel(\"Time\")\n",
 98 |         "    plt.ylabel(\"Value\")\n",
 99 |         "    plt.grid(True)\n",
100 |         "\n",
101 |         "def trend(time, slope=0):\n",
102 |         "    return slope * time\n",
103 |         "\n",
104 |         "def seasonal_pattern(season_time):\n",
105 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
106 |         "    return np.where(season_time < 0.4,\n",
107 |         "                    np.cos(season_time * 2 * np.pi),\n",
108 |         "                    1 / np.exp(3 * season_time))\n",
109 |         "\n",
110 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
111 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
112 |         "    season_time = ((time + phase) % period) / period\n",
113 |         "    return amplitude * seasonal_pattern(season_time)\n",
114 |         "\n",
115 |         "def noise(time, noise_level=1, seed=None):\n",
116 |         "    rnd = np.random.RandomState(seed)\n",
117 |         "    return rnd.randn(len(time)) * noise_level\n",
118 |         "\n",
119 |         "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
120 |         "baseline = 10\n",
121 |         "series = trend(time, 0.1)  \n",
122 |         "baseline = 10\n",
123 |         "amplitude = 40\n",
124 |         "slope = 0.05\n",
125 |         "noise_level = 5\n",
126 |         "\n",
127 |         "# Create the series\n",
128 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
129 |         "# Update with noise\n",
130 |         "series += noise(time, noise_level, seed=42)\n",
131 |         "\n",
132 |         "plt.figure(figsize=(10, 6))\n",
133 |         "plot_series(time, series)\n",
134 |         "plt.show()"
135 |       ],
136 |       "execution_count": null,
137 |       "outputs": []
138 |     },
139 |     {
140 |       "cell_type": "markdown",
141 |       "metadata": {
142 |         "id": "UfdyqJJ1VZVu"
143 |       },
144 |       "source": [
145 |         "Now that we have the time series, let's split it so we can start forecasting"
146 |       ]
147 |     },
148 |     {
149 |       "cell_type": "code",
150 |       "metadata": {
151 |         "id": "_w0eKap5uFNP"
152 |       },
153 |       "source": [
154 |         "split_time = 1000\n",
155 |         "time_train = time[:split_time]\n",
156 |         "x_train = series[:split_time]\n",
157 |         "time_valid = time[split_time:]\n",
158 |         "x_valid = series[split_time:]\n",
159 |         "plt.figure(figsize=(10, 6))\n",
160 |         "plot_series(time_train, x_train)\n",
161 |         "plt.show()\n",
162 |         "\n",
163 |         "plt.figure(figsize=(10, 6))\n",
164 |         "plot_series(time_valid, x_valid)\n",
165 |         "plt.show()"
166 |       ],
167 |       "execution_count": null,
168 |       "outputs": []
169 |     },
170 |     {
171 |       "cell_type": "markdown",
172 |       "metadata": {
173 |         "id": "bjD8ncEZbjEW"
174 |       },
175 |       "source": [
176 |         "# Naive Forecast"
177 |       ]
178 |     },
179 |     {
180 |       "cell_type": "code",
181 |       "metadata": {
182 |         "id": "Pj_-uCeYxcAb"
183 |       },
184 |       "source": [
185 |         "naive_forecast = series[split_time - 1:-1]"
186 |       ],
187 |       "execution_count": null,
188 |       "outputs": []
189 |     },
190 |     {
191 |       "cell_type": "code",
192 |       "metadata": {
193 |         "id": "JtxwHj9Ig0jT"
194 |       },
195 |       "source": [
196 |         "plt.figure(figsize=(10, 6))\n",
197 |         "plot_series(time_valid, x_valid)\n",
198 |         "plot_series(time_valid, naive_forecast)"
199 |       ],
200 |       "execution_count": null,
201 |       "outputs": []
202 |     },
203 |     {
204 |       "cell_type": "markdown",
205 |       "metadata": {
206 |         "id": "fw1SP5WeuixH"
207 |       },
208 |       "source": [
209 |         "Let's zoom in on the start of the validation period:"
210 |       ]
211 |     },
212 |     {
213 |       "cell_type": "code",
214 |       "metadata": {
215 |         "id": "D0MKg7FNug9V"
216 |       },
217 |       "source": [
218 |         "plt.figure(figsize=(10, 6))\n",
219 |         "plot_series(time_valid, x_valid, start=0, end=150)\n",
220 |         "plot_series(time_valid, naive_forecast, start=1, end=151)"
221 |       ],
222 |       "execution_count": null,
223 |       "outputs": []
224 |     },
225 |     {
226 |       "cell_type": "markdown",
227 |       "metadata": {
228 |         "id": "35gIlQLfu0TT"
229 |       },
230 |       "source": [
231 |         "You can see that the naive forecast lags 1 step behind the time series."
232 |       ]
233 |     },
234 |     {
235 |       "cell_type": "markdown",
236 |       "metadata": {
237 |         "id": "Uh_7244Gsxfx"
238 |       },
239 |       "source": [
240 |         "Now let's compute the mean squared error and the mean absolute error between the forecasts and the predictions in the validation period:"
241 |       ]
242 |     },
243 |     {
244 |       "cell_type": "code",
245 |       "metadata": {
246 |         "id": "byNnC7IbsnMZ"
247 |       },
248 |       "source": [
249 |         "print(keras.metrics.mean_squared_error(x_valid, naive_forecast).numpy())\n",
250 |         "print(keras.metrics.mean_absolute_error(x_valid, naive_forecast).numpy())"
251 |       ],
252 |       "execution_count": null,
253 |       "outputs": []
254 |     },
255 |     {
256 |       "cell_type": "markdown",
257 |       "metadata": {
258 |         "id": "WGPBC9QttI1u"
259 |       },
260 |       "source": [
261 |         "That's our baseline, now let's try a moving average:"
262 |       ]
263 |     },
264 |     {
265 |       "cell_type": "code",
266 |       "metadata": {
267 |         "id": "YGz5UsUdf2tV"
268 |       },
269 |       "source": [
270 |         "def moving_average_forecast(series, window_size):\n",
271 |         "  \"\"\"Forecasts the mean of the last few values.\n",
272 |         "     If window_size=1, then this is equivalent to naive forecast\"\"\"\n",
273 |         "  forecast = []\n",
274 |         "  for time in range(len(series) - window_size):\n",
275 |         "    forecast.append(series[time:time + window_size].mean())\n",
276 |         "  return np.array(forecast)"
277 |       ],
278 |       "execution_count": null,
279 |       "outputs": []
280 |     },
281 |     {
282 |       "cell_type": "code",
283 |       "metadata": {
284 |         "id": "HHFhGXQji7_r"
285 |       },
286 |       "source": [
287 |         "moving_avg = moving_average_forecast(series, 30)[split_time - 30:]\n",
288 |         "\n",
289 |         "plt.figure(figsize=(10, 6))\n",
290 |         "plot_series(time_valid, x_valid)\n",
291 |         "plot_series(time_valid, moving_avg)"
292 |       ],
293 |       "execution_count": null,
294 |       "outputs": []
295 |     },
296 |     {
297 |       "cell_type": "code",
298 |       "metadata": {
299 |         "id": "wG7pTAd7z0e8"
300 |       },
301 |       "source": [
302 |         "print(keras.metrics.mean_squared_error(x_valid, moving_avg).numpy())\n",
303 |         "print(keras.metrics.mean_absolute_error(x_valid, moving_avg).numpy())"
304 |       ],
305 |       "execution_count": null,
306 |       "outputs": []
307 |     },
308 |     {
309 |       "cell_type": "markdown",
310 |       "metadata": {
311 |         "id": "JMYPnJqwz8nS"
312 |       },
313 |       "source": [
314 |         "That's worse than naive forecast! The moving average does not anticipate trend or seasonality, so let's try to remove them by using differencing. Since the seasonality period is 365 days, we will subtract the value at time *t* – 365 from the value at time *t*."
315 |       ]
316 |     },
317 |     {
318 |       "cell_type": "code",
319 |       "metadata": {
320 |         "id": "5pqySF7-rJR4"
321 |       },
322 |       "source": [
323 |         "diff_series = (series[365:] - series[:-365])\n",
324 |         "diff_time = time[365:]\n",
325 |         "\n",
326 |         "plt.figure(figsize=(10, 6))\n",
327 |         "plot_series(diff_time, diff_series)\n",
328 |         "plt.show()"
329 |       ],
330 |       "execution_count": null,
331 |       "outputs": []
332 |     },
333 |     {
334 |       "cell_type": "markdown",
335 |       "metadata": {
336 |         "id": "xPlPlS7DskWg"
337 |       },
338 |       "source": [
339 |         "Great, the trend and seasonality seem to be gone, so now we can use the moving average:"
340 |       ]
341 |     },
342 |     {
343 |       "cell_type": "code",
344 |       "metadata": {
345 |         "id": "QmZpz7arsjbb"
346 |       },
347 |       "source": [
348 |         "diff_moving_avg = moving_average_forecast(diff_series, 50)[split_time - 365 - 50:]\n",
349 |         "\n",
350 |         "plt.figure(figsize=(10, 6))\n",
351 |         "plot_series(time_valid, diff_series[split_time - 365:])\n",
352 |         "plot_series(time_valid, diff_moving_avg)\n",
353 |         "plt.show()"
354 |       ],
355 |       "execution_count": null,
356 |       "outputs": []
357 |     },
358 |     {
359 |       "cell_type": "markdown",
360 |       "metadata": {
361 |         "id": "Gno9S2lyecnc"
362 |       },
363 |       "source": [
364 |         "Now let's bring back the trend and seasonality by adding the past values from t – 365:"
365 |       ]
366 |     },
367 |     {
368 |       "cell_type": "code",
369 |       "metadata": {
370 |         "id": "Dv6RWFq7TFGB"
371 |       },
372 |       "source": [
373 |         "diff_moving_avg_plus_past = series[split_time - 365:-365] + diff_moving_avg\n",
374 |         "\n",
375 |         "plt.figure(figsize=(10, 6))\n",
376 |         "plot_series(time_valid, x_valid)\n",
377 |         "plot_series(time_valid, diff_moving_avg_plus_past)\n",
378 |         "plt.show()"
379 |       ],
380 |       "execution_count": null,
381 |       "outputs": []
382 |     },
383 |     {
384 |       "cell_type": "code",
385 |       "metadata": {
386 |         "id": "59jmBrwcTFCx"
387 |       },
388 |       "source": [
389 |         "print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_past).numpy())\n",
390 |         "print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_past).numpy())"
391 |       ],
392 |       "execution_count": null,
393 |       "outputs": []
394 |     },
395 |     {
396 |       "cell_type": "markdown",
397 |       "metadata": {
398 |         "id": "vx9Et1Hkeusl"
399 |       },
400 |       "source": [
401 |         "Better than naive forecast, good. However the forecasts look a bit too random, because we're just adding past values, which were noisy. Let's use a moving averaging on past values to remove some of the noise:"
402 |       ]
403 |     },
404 |     {
405 |       "cell_type": "code",
406 |       "metadata": {
407 |         "id": "K81dtROoTE_r"
408 |       },
409 |       "source": [
410 |         "diff_moving_avg_plus_smooth_past = moving_average_forecast(series[split_time - 370:-360], 10) + diff_moving_avg\n",
411 |         "\n",
412 |         "plt.figure(figsize=(10, 6))\n",
413 |         "plot_series(time_valid, x_valid)\n",
414 |         "plot_series(time_valid, diff_moving_avg_plus_smooth_past)\n",
415 |         "plt.show()"
416 |       ],
417 |       "execution_count": null,
418 |       "outputs": []
419 |     },
420 |     {
421 |       "cell_type": "code",
422 |       "metadata": {
423 |         "id": "iN2MsBxWTE3m"
424 |       },
425 |       "source": [
426 |         "print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_smooth_past).numpy())\n",
427 |         "print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_smooth_past).numpy())"
428 |       ],
429 |       "execution_count": null,
430 |       "outputs": []
431 |     }
432 |   ]
433 | }


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_2_Exercise_Answer_ts_prediction_with_DNN.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "accelerator": "GPU",
  6 |     "colab": {
  7 |       "name": "S+P_Week_2_Exercise_Answer.ipynb",
  8 |       "provenance": [],
  9 |       "toc_visible": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "display_name": "Python 3",
 13 |       "name": "python3"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "metadata": {
 20 |         "id": "zX4Kg8DUTKWO"
 21 |       },
 22 |       "source": [
 23 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 24 |         "# you may not use this file except in compliance with the License.\n",
 25 |         "# You may obtain a copy of the License at\n",
 26 |         "#\n",
 27 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 28 |         "#\n",
 29 |         "# Unless required by applicable law or agreed to in writing, software\n",
 30 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 31 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 32 |         "# See the License for the specific language governing permissions and\n",
 33 |         "# limitations under the License."
 34 |       ],
 35 |       "execution_count": null,
 36 |       "outputs": []
 37 |     },
 38 |     {
 39 |       "cell_type": "code",
 40 |       "metadata": {
 41 |         "cellView": "both",
 42 |         "id": "D1J15Vh_1Jih"
 43 |       },
 44 |       "source": [
 45 |         "!pip install tf-nightly-2.0-preview\n"
 46 |       ],
 47 |       "execution_count": null,
 48 |       "outputs": []
 49 |     },
 50 |     {
 51 |       "cell_type": "code",
 52 |       "metadata": {
 53 |         "id": "BOjujz601HcS"
 54 |       },
 55 |       "source": [
 56 |         "import tensorflow as tf\n",
 57 |         "import numpy as np\n",
 58 |         "import matplotlib.pyplot as plt\n",
 59 |         "print(tf.__version__)"
 60 |       ],
 61 |       "execution_count": null,
 62 |       "outputs": []
 63 |     },
 64 |     {
 65 |       "cell_type": "code",
 66 |       "metadata": {
 67 |         "id": "Zswl7jRtGzkk"
 68 |       },
 69 |       "source": [
 70 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 71 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 72 |         "    plt.xlabel(\"Time\")\n",
 73 |         "    plt.ylabel(\"Value\")\n",
 74 |         "    plt.grid(False)\n",
 75 |         "\n",
 76 |         "def trend(time, slope=0):\n",
 77 |         "    return slope * time\n",
 78 |         "\n",
 79 |         "def seasonal_pattern(season_time):\n",
 80 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
 81 |         "    return np.where(season_time < 0.1,\n",
 82 |         "                    np.cos(season_time * 6 * np.pi),\n",
 83 |         "                    2 / np.exp(9 * season_time))\n",
 84 |         "\n",
 85 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
 86 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
 87 |         "    season_time = ((time + phase) % period) / period\n",
 88 |         "    return amplitude * seasonal_pattern(season_time)\n",
 89 |         "\n",
 90 |         "def noise(time, noise_level=1, seed=None):\n",
 91 |         "    rnd = np.random.RandomState(seed)\n",
 92 |         "    return rnd.randn(len(time)) * noise_level\n",
 93 |         "\n",
 94 |         "time = np.arange(10 * 365 + 1, dtype=\"float32\")\n",
 95 |         "baseline = 10\n",
 96 |         "series = trend(time, 0.1)  \n",
 97 |         "baseline = 10\n",
 98 |         "amplitude = 40\n",
 99 |         "slope = 0.005\n",
100 |         "noise_level = 3\n",
101 |         "\n",
102 |         "# Create the series\n",
103 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
104 |         "# Update with noise\n",
105 |         "series += noise(time, noise_level, seed=51)\n",
106 |         "\n",
107 |         "split_time = 3000\n",
108 |         "time_train = time[:split_time]\n",
109 |         "x_train = series[:split_time]\n",
110 |         "time_valid = time[split_time:]\n",
111 |         "x_valid = series[split_time:]\n",
112 |         "\n",
113 |         "window_size = 20\n",
114 |         "batch_size = 32\n",
115 |         "shuffle_buffer_size = 1000\n",
116 |         "\n",
117 |         "plot_series(time, series)"
118 |       ],
119 |       "execution_count": null,
120 |       "outputs": []
121 |     },
122 |     {
123 |       "cell_type": "code",
124 |       "metadata": {
125 |         "id": "4sTTIOCbyShY"
126 |       },
127 |       "source": [
128 |         "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
129 |         "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
130 |         "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
131 |         "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
132 |         "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
133 |         "  dataset = dataset.batch(batch_size).prefetch(1)\n",
134 |         "  return dataset"
135 |       ],
136 |       "execution_count": null,
137 |       "outputs": []
138 |     },
139 |     {
140 |       "cell_type": "code",
141 |       "metadata": {
142 |         "id": "TW-vT7eLYAdb"
143 |       },
144 |       "source": [
145 |         "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
146 |         "\n",
147 |         "\n",
148 |         "model = tf.keras.models.Sequential([\n",
149 |         "    tf.keras.layers.Dense(100, input_shape=[window_size], activation=\"relu\"), \n",
150 |         "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
151 |         "    tf.keras.layers.Dense(1)\n",
152 |         "])\n",
153 |         "\n",
154 |         "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
155 |         "model.fit(dataset,epochs=100,verbose=0)\n"
156 |       ],
157 |       "execution_count": null,
158 |       "outputs": []
159 |     },
160 |     {
161 |       "cell_type": "code",
162 |       "metadata": {
163 |         "id": "efhco2rYyIFF"
164 |       },
165 |       "source": [
166 |         "forecast = []\n",
167 |         "for time in range(len(series) - window_size):\n",
168 |         "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
169 |         "\n",
170 |         "forecast = forecast[split_time-window_size:]\n",
171 |         "results = np.array(forecast)[:, 0, 0]\n",
172 |         "\n",
173 |         "\n",
174 |         "plt.figure(figsize=(10, 6))\n",
175 |         "\n",
176 |         "plot_series(time_valid, x_valid)\n",
177 |         "plot_series(time_valid, results)"
178 |       ],
179 |       "execution_count": null,
180 |       "outputs": []
181 |     },
182 |     {
183 |       "cell_type": "code",
184 |       "metadata": {
185 |         "id": "-kT6j186YO6K"
186 |       },
187 |       "source": [
188 |         "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
189 |       ],
190 |       "execution_count": null,
191 |       "outputs": []
192 |     }
193 |   ]
194 | }


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_2_Lesson_1_ts_data_preparation_for_ML.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "accelerator": "GPU",
  6 |     "colab": {
  7 |       "name": "S+P Week 2 Lesson 1.ipynb",
  8 |       "provenance": [],
  9 |       "toc_visible": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "display_name": "Python 3",
 13 |       "name": "python3"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "metadata": {
 20 |         "id": "zX4Kg8DUTKWO"
 21 |       },
 22 |       "source": [
 23 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 24 |         "# you may not use this file except in compliance with the License.\n",
 25 |         "# You may obtain a copy of the License at\n",
 26 |         "#\n",
 27 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 28 |         "#\n",
 29 |         "# Unless required by applicable law or agreed to in writing, software\n",
 30 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 31 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 32 |         "# See the License for the specific language governing permissions and\n",
 33 |         "# limitations under the License."
 34 |       ],
 35 |       "execution_count": null,
 36 |       "outputs": []
 37 |     },
 38 |     {
 39 |       "cell_type": "code",
 40 |       "metadata": {
 41 |         "id": "s6eq-RBcQ_Zr"
 42 |       },
 43 |       "source": [
 44 |         "try:\n",
 45 |         "  # %tensorflow_version only exists in Colab.\n",
 46 |         "  %tensorflow_version 2.x\n",
 47 |         "except Exception:\n",
 48 |         "  pass"
 49 |       ],
 50 |       "execution_count": null,
 51 |       "outputs": []
 52 |     },
 53 |     {
 54 |       "cell_type": "code",
 55 |       "metadata": {
 56 |         "id": "BOjujz601HcS"
 57 |       },
 58 |       "source": [
 59 |         "import tensorflow as tf\n",
 60 |         "import numpy as np\n",
 61 |         "import matplotlib.pyplot as plt\n",
 62 |         "print(tf.__version__)"
 63 |       ],
 64 |       "execution_count": null,
 65 |       "outputs": []
 66 |     },
 67 |     {
 68 |       "cell_type": "code",
 69 |       "metadata": {
 70 |         "id": "asEdslR_05O_"
 71 |       },
 72 |       "source": [
 73 |         "dataset = tf.data.Dataset.range(10)\n",
 74 |         "for val in dataset:\n",
 75 |         "   print(val.numpy())"
 76 |       ],
 77 |       "execution_count": null,
 78 |       "outputs": []
 79 |     },
 80 |     {
 81 |       "cell_type": "code",
 82 |       "metadata": {
 83 |         "id": "Lrv_ghSt1lgQ"
 84 |       },
 85 |       "source": [
 86 |         "dataset = tf.data.Dataset.range(10)\n",
 87 |         "dataset = dataset.window(5, shift=1)\n",
 88 |         "for window_dataset in dataset:\n",
 89 |         "  for val in window_dataset:\n",
 90 |         "    print(val.numpy(), end=\" \")\n",
 91 |         "  print()"
 92 |       ],
 93 |       "execution_count": null,
 94 |       "outputs": []
 95 |     },
 96 |     {
 97 |       "cell_type": "code",
 98 |       "metadata": {
 99 |         "id": "QLEq6MG-2DN2"
100 |       },
101 |       "source": [
102 |         "dataset = tf.data.Dataset.range(10)\n",
103 |         "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
104 |         "for window_dataset in dataset:\n",
105 |         "  for val in window_dataset:\n",
106 |         "    print(val.numpy(), end=\" \")\n",
107 |         "  print()"
108 |       ],
109 |       "execution_count": null,
110 |       "outputs": []
111 |     },
112 |     {
113 |       "cell_type": "code",
114 |       "metadata": {
115 |         "id": "PJ9CAHlJ2ODe"
116 |       },
117 |       "source": [
118 |         "dataset = tf.data.Dataset.range(10)\n",
119 |         "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
120 |         "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
121 |         "for window in dataset:\n",
122 |         "  print(window.numpy())\n"
123 |       ],
124 |       "execution_count": null,
125 |       "outputs": []
126 |     },
127 |     {
128 |       "cell_type": "code",
129 |       "metadata": {
130 |         "id": "DryEZ2Mz2nNV"
131 |       },
132 |       "source": [
133 |         "dataset = tf.data.Dataset.range(10)\n",
134 |         "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
135 |         "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
136 |         "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
137 |         "for x,y in dataset:\n",
138 |         "  print(x.numpy(), y.numpy())"
139 |       ],
140 |       "execution_count": null,
141 |       "outputs": []
142 |     },
143 |     {
144 |       "cell_type": "code",
145 |       "metadata": {
146 |         "id": "1tl-0BOKkEtk"
147 |       },
148 |       "source": [
149 |         "dataset = tf.data.Dataset.range(10)\n",
150 |         "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
151 |         "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
152 |         "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
153 |         "dataset = dataset.shuffle(buffer_size=10)\n",
154 |         "for x,y in dataset:\n",
155 |         "  print(x.numpy(), y.numpy())\n"
156 |       ],
157 |       "execution_count": null,
158 |       "outputs": []
159 |     },
160 |     {
161 |       "cell_type": "code",
162 |       "metadata": {
163 |         "id": "Wa0PNwxMGapy"
164 |       },
165 |       "source": [
166 |         "dataset = tf.data.Dataset.range(10)\n",
167 |         "dataset = dataset.window(5, shift=1, drop_remainder=True)\n",
168 |         "dataset = dataset.flat_map(lambda window: window.batch(5))\n",
169 |         "dataset = dataset.map(lambda window: (window[:-1], window[-1:]))\n",
170 |         "dataset = dataset.shuffle(buffer_size=10)\n",
171 |         "dataset = dataset.batch(2).prefetch(1)\n",
172 |         "for x,y in dataset:\n",
173 |         "  print(\"x = \", x.numpy())\n",
174 |         "  print(\"y = \", y.numpy())\n"
175 |       ],
176 |       "execution_count": null,
177 |       "outputs": []
178 |     }
179 |   ]
180 | }


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_2_Lesson_2_linear_regression_ts_data.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "S+P Week 2 Lesson 2.ipynb",
  7 |       "provenance": [],
  8 |       "toc_visible": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "display_name": "Python 3",
 12 |       "name": "python3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "code",
 18 |       "metadata": {
 19 |         "id": "zX4Kg8DUTKWO"
 20 |       },
 21 |       "source": [
 22 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 23 |         "# you may not use this file except in compliance with the License.\n",
 24 |         "# You may obtain a copy of the License at\n",
 25 |         "#\n",
 26 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 27 |         "#\n",
 28 |         "# Unless required by applicable law or agreed to in writing, software\n",
 29 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 30 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 31 |         "# See the License for the specific language governing permissions and\n",
 32 |         "# limitations under the License."
 33 |       ],
 34 |       "execution_count": null,
 35 |       "outputs": []
 36 |     },
 37 |     {
 38 |       "cell_type": "markdown",
 39 |       "metadata": {
 40 |         "id": "view-in-github"
 41 |       },
 42 |       "source": [
 43 |         "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%202%20Lesson%202.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 44 |       ]
 45 |     },
 46 |     {
 47 |       "cell_type": "code",
 48 |       "metadata": {
 49 |         "id": "-pwam8szRReT"
 50 |       },
 51 |       "source": [
 52 |         "try:\n",
 53 |         "  # %tensorflow_version only exists in Colab.\n",
 54 |         "  %tensorflow_version 2.x\n",
 55 |         "except Exception:\n",
 56 |         "  pass"
 57 |       ],
 58 |       "execution_count": null,
 59 |       "outputs": []
 60 |     },
 61 |     {
 62 |       "cell_type": "code",
 63 |       "metadata": {
 64 |         "id": "BOjujz601HcS"
 65 |       },
 66 |       "source": [
 67 |         "import tensorflow as tf\n",
 68 |         "import numpy as np\n",
 69 |         "import matplotlib.pyplot as plt\n",
 70 |         "print(tf.__version__)"
 71 |       ],
 72 |       "execution_count": null,
 73 |       "outputs": []
 74 |     },
 75 |     {
 76 |       "cell_type": "code",
 77 |       "metadata": {
 78 |         "id": "Zswl7jRtGzkk"
 79 |       },
 80 |       "source": [
 81 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 82 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 83 |         "    plt.xlabel(\"Time\")\n",
 84 |         "    plt.ylabel(\"Value\")\n",
 85 |         "    plt.grid(True)\n",
 86 |         "\n",
 87 |         "def trend(time, slope=0):\n",
 88 |         "    return slope * time\n",
 89 |         "\n",
 90 |         "def seasonal_pattern(season_time):\n",
 91 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
 92 |         "    return np.where(season_time < 0.4,\n",
 93 |         "                    np.cos(season_time * 2 * np.pi),\n",
 94 |         "                    1 / np.exp(3 * season_time))\n",
 95 |         "\n",
 96 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
 97 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
 98 |         "    season_time = ((time + phase) % period) / period\n",
 99 |         "    return amplitude * seasonal_pattern(season_time)\n",
100 |         "\n",
101 |         "def noise(time, noise_level=1, seed=None):\n",
102 |         "    rnd = np.random.RandomState(seed)\n",
103 |         "    return rnd.randn(len(time)) * noise_level\n",
104 |         "\n",
105 |         "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
106 |         "baseline = 10\n",
107 |         "series = trend(time, 0.1)  \n",
108 |         "baseline = 10\n",
109 |         "amplitude = 40\n",
110 |         "slope = 0.05\n",
111 |         "noise_level = 5\n",
112 |         "\n",
113 |         "# Create the series\n",
114 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
115 |         "# Update with noise\n",
116 |         "series += noise(time, noise_level, seed=42)\n",
117 |         "\n",
118 |         "split_time = 1000\n",
119 |         "time_train = time[:split_time]\n",
120 |         "x_train = series[:split_time]\n",
121 |         "time_valid = time[split_time:]\n",
122 |         "x_valid = series[split_time:]\n",
123 |         "\n",
124 |         "window_size = 20\n",
125 |         "batch_size = 32\n",
126 |         "shuffle_buffer_size = 1000"
127 |       ],
128 |       "execution_count": null,
129 |       "outputs": []
130 |     },
131 |     {
132 |       "cell_type": "code",
133 |       "metadata": {
134 |         "id": "4sTTIOCbyShY"
135 |       },
136 |       "source": [
137 |         "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
138 |         "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
139 |         "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
140 |         "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
141 |         "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
142 |         "  dataset = dataset.batch(batch_size).prefetch(1)\n",
143 |         "  return dataset"
144 |       ],
145 |       "execution_count": null,
146 |       "outputs": []
147 |     },
148 |     {
149 |       "cell_type": "code",
150 |       "metadata": {
151 |         "id": "ou-WmE2AXu6B"
152 |       },
153 |       "source": [
154 |         "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
155 |         "print(dataset)\n",
156 |         "l0 = tf.keras.layers.Dense(1, input_shape=[window_size])\n",
157 |         "model = tf.keras.models.Sequential([l0])\n",
158 |         "\n",
159 |         "\n",
160 |         "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
161 |         "model.fit(dataset,epochs=100,verbose=0)\n",
162 |         "\n",
163 |         "print(\"Layer weights {}\".format(l0.get_weights()))\n"
164 |       ],
165 |       "execution_count": null,
166 |       "outputs": []
167 |     },
168 |     {
169 |       "cell_type": "code",
170 |       "metadata": {
171 |         "id": "-gtVJuLVxR-M"
172 |       },
173 |       "source": [
174 |         "forecast = []\n",
175 |         "\n",
176 |         "for time in range(len(series) - window_size):\n",
177 |         "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
178 |         "\n",
179 |         "forecast = forecast[split_time-window_size:]\n",
180 |         "results = np.array(forecast)[:, 0, 0]\n",
181 |         "\n",
182 |         "\n",
183 |         "plt.figure(figsize=(10, 6))\n",
184 |         "\n",
185 |         "plot_series(time_valid, x_valid)\n",
186 |         "plot_series(time_valid, results)"
187 |       ],
188 |       "execution_count": null,
189 |       "outputs": []
190 |     },
191 |     {
192 |       "cell_type": "code",
193 |       "metadata": {
194 |         "id": "hR2BO0Dai_ZT"
195 |       },
196 |       "source": [
197 |         "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
198 |       ],
199 |       "execution_count": null,
200 |       "outputs": []
201 |     }
202 |   ]
203 | }


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_2_Lesson_3_ts_prediction_with_DNN.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "S+P Week 2 Lesson 3.ipynb",
  7 |       "provenance": [],
  8 |       "toc_visible": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "display_name": "Python 3",
 12 |       "name": "python3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "cell_type": "code",
 18 |       "metadata": {
 19 |         "id": "zX4Kg8DUTKWO"
 20 |       },
 21 |       "source": [
 22 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 23 |         "# you may not use this file except in compliance with the License.\n",
 24 |         "# You may obtain a copy of the License at\n",
 25 |         "#\n",
 26 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 27 |         "#\n",
 28 |         "# Unless required by applicable law or agreed to in writing, software\n",
 29 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 30 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 31 |         "# See the License for the specific language governing permissions and\n",
 32 |         "# limitations under the License."
 33 |       ],
 34 |       "execution_count": null,
 35 |       "outputs": []
 36 |     },
 37 |     {
 38 |       "cell_type": "markdown",
 39 |       "metadata": {
 40 |         "id": "view-in-github"
 41 |       },
 42 |       "source": [
 43 |         "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%202%20Lesson%203.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 44 |       ]
 45 |     },
 46 |     {
 47 |       "cell_type": "code",
 48 |       "metadata": {
 49 |         "cellView": "both",
 50 |         "id": "D1J15Vh_1Jih"
 51 |       },
 52 |       "source": [
 53 |         "try:\n",
 54 |         "  # %tensorflow_version only exists in Colab.\n",
 55 |         "  %tensorflow_version 2.x\n",
 56 |         "except Exception:\n",
 57 |         "  pass\n"
 58 |       ],
 59 |       "execution_count": null,
 60 |       "outputs": []
 61 |     },
 62 |     {
 63 |       "cell_type": "code",
 64 |       "metadata": {
 65 |         "id": "BOjujz601HcS"
 66 |       },
 67 |       "source": [
 68 |         "import tensorflow as tf\n",
 69 |         "import numpy as np\n",
 70 |         "import matplotlib.pyplot as plt\n",
 71 |         "print(tf.__version__)"
 72 |       ],
 73 |       "execution_count": null,
 74 |       "outputs": []
 75 |     },
 76 |     {
 77 |       "cell_type": "code",
 78 |       "metadata": {
 79 |         "id": "Zswl7jRtGzkk"
 80 |       },
 81 |       "source": [
 82 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 83 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 84 |         "    plt.xlabel(\"Time\")\n",
 85 |         "    plt.ylabel(\"Value\")\n",
 86 |         "    plt.grid(True)\n",
 87 |         "\n",
 88 |         "def trend(time, slope=0):\n",
 89 |         "    return slope * time\n",
 90 |         "\n",
 91 |         "def seasonal_pattern(season_time):\n",
 92 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
 93 |         "    return np.where(season_time < 0.4,\n",
 94 |         "                    np.cos(season_time * 2 * np.pi),\n",
 95 |         "                    1 / np.exp(3 * season_time))\n",
 96 |         "\n",
 97 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
 98 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
 99 |         "    season_time = ((time + phase) % period) / period\n",
100 |         "    return amplitude * seasonal_pattern(season_time)\n",
101 |         "\n",
102 |         "def noise(time, noise_level=1, seed=None):\n",
103 |         "    rnd = np.random.RandomState(seed)\n",
104 |         "    return rnd.randn(len(time)) * noise_level\n",
105 |         "\n",
106 |         "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
107 |         "baseline = 10\n",
108 |         "series = trend(time, 0.1)  \n",
109 |         "baseline = 10\n",
110 |         "amplitude = 20\n",
111 |         "slope = 0.09\n",
112 |         "noise_level = 5\n",
113 |         "\n",
114 |         "# Create the series\n",
115 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
116 |         "# Update with noise\n",
117 |         "series += noise(time, noise_level, seed=42)\n",
118 |         "\n",
119 |         "split_time = 1000\n",
120 |         "time_train = time[:split_time]\n",
121 |         "x_train = series[:split_time]\n",
122 |         "time_valid = time[split_time:]\n",
123 |         "x_valid = series[split_time:]\n",
124 |         "\n",
125 |         "window_size = 20\n",
126 |         "batch_size = 32\n",
127 |         "shuffle_buffer_size = 1000"
128 |       ],
129 |       "execution_count": null,
130 |       "outputs": []
131 |     },
132 |     {
133 |       "cell_type": "code",
134 |       "metadata": {
135 |         "id": "tVH2XEt4yA4m"
136 |       },
137 |       "source": [
138 |         "plt.figure(figsize=(10, 6))\n",
139 |         "plot_series(time_valid, x_valid)\n"
140 |       ],
141 |       "execution_count": null,
142 |       "outputs": []
143 |     },
144 |     {
145 |       "cell_type": "code",
146 |       "metadata": {
147 |         "id": "4sTTIOCbyShY"
148 |       },
149 |       "source": [
150 |         "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
151 |         "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
152 |         "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
153 |         "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
154 |         "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
155 |         "  dataset = dataset.batch(batch_size).prefetch(1)\n",
156 |         "  return dataset"
157 |       ],
158 |       "execution_count": null,
159 |       "outputs": []
160 |     },
161 |     {
162 |       "cell_type": "code",
163 |       "metadata": {
164 |         "id": "TW-vT7eLYAdb"
165 |       },
166 |       "source": [
167 |         "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
168 |         "\n",
169 |         "\n",
170 |         "model = tf.keras.models.Sequential([\n",
171 |         "    tf.keras.layers.Dense(10, input_shape=[window_size], activation=\"relu\"), \n",
172 |         "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
173 |         "    tf.keras.layers.Dense(1)\n",
174 |         "])\n",
175 |         "\n",
176 |         "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
177 |         "model.fit(dataset,epochs=100,verbose=0)\n"
178 |       ],
179 |       "execution_count": null,
180 |       "outputs": []
181 |     },
182 |     {
183 |       "cell_type": "code",
184 |       "metadata": {
185 |         "id": "efhco2rYyIFF"
186 |       },
187 |       "source": [
188 |         "forecast = []\n",
189 |         "for time in range(len(series) - window_size):\n",
190 |         "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
191 |         "\n",
192 |         "forecast = forecast[split_time-window_size:]\n",
193 |         "results = np.array(forecast)[:, 0, 0]\n",
194 |         "\n",
195 |         "\n",
196 |         "plt.figure(figsize=(10, 6))\n",
197 |         "\n",
198 |         "plot_series(time_valid, x_valid)\n",
199 |         "plot_series(time_valid, results)"
200 |       ],
201 |       "execution_count": null,
202 |       "outputs": []
203 |     },
204 |     {
205 |       "cell_type": "code",
206 |       "metadata": {
207 |         "id": "-kT6j186YO6K"
208 |       },
209 |       "source": [
210 |         "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
211 |       ],
212 |       "execution_count": null,
213 |       "outputs": []
214 |     },
215 |     {
216 |       "cell_type": "code",
217 |       "metadata": {
218 |         "id": "tnCe_nBKu7RB"
219 |       },
220 |       "source": [
221 |         "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
222 |         "\n",
223 |         "\n",
224 |         "model = tf.keras.models.Sequential([\n",
225 |         "    tf.keras.layers.Dense(10, input_shape=[window_size], activation=\"relu\"), \n",
226 |         "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
227 |         "    tf.keras.layers.Dense(1)\n",
228 |         "])\n",
229 |         "\n",
230 |         "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
231 |         "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
232 |         "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
233 |         "model.compile(loss=\"mse\", optimizer=optimizer)\n",
234 |         "history = model.fit(dataset, epochs=100, callbacks=[lr_schedule], verbose=0)"
235 |       ],
236 |       "execution_count": null,
237 |       "outputs": []
238 |     },
239 |     {
240 |       "cell_type": "code",
241 |       "metadata": {
242 |         "id": "2ZaNsM2IgCd_"
243 |       },
244 |       "source": [
245 |         "lrs = 1e-8 * (10 ** (np.arange(100) / 20))\n",
246 |         "plt.semilogx(lrs, history.history[\"loss\"])\n",
247 |         "plt.axis([1e-8, 1e-3, 0, 300])"
248 |       ],
249 |       "execution_count": null,
250 |       "outputs": []
251 |     },
252 |     {
253 |       "cell_type": "code",
254 |       "metadata": {
255 |         "id": "QDwW0Q7ovYK1"
256 |       },
257 |       "source": [
258 |         "window_size = 30\n",
259 |         "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
260 |         "\n",
261 |         "model = tf.keras.models.Sequential([\n",
262 |         "  tf.keras.layers.Dense(10, activation=\"relu\", input_shape=[window_size]),\n",
263 |         "  tf.keras.layers.Dense(10, activation=\"relu\"),\n",
264 |         "  tf.keras.layers.Dense(1)\n",
265 |         "])\n",
266 |         "\n",
267 |         "optimizer = tf.keras.optimizers.SGD(lr=8e-6, momentum=0.9)\n",
268 |         "model.compile(loss=\"mse\", optimizer=optimizer)\n",
269 |         "history = model.fit(dataset, epochs=500, verbose=0)"
270 |       ],
271 |       "execution_count": null,
272 |       "outputs": []
273 |     },
274 |     {
275 |       "cell_type": "code",
276 |       "metadata": {
277 |         "id": "iXBMO1HM9AHX"
278 |       },
279 |       "source": [
280 |         "loss = history.history['loss']\n",
281 |         "epochs = range(len(loss))\n",
282 |         "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
283 |         "plt.show()"
284 |       ],
285 |       "execution_count": null,
286 |       "outputs": []
287 |     },
288 |     {
289 |       "cell_type": "code",
290 |       "metadata": {
291 |         "id": "xakiRU7R7WAo"
292 |       },
293 |       "source": [
294 |         "# Plot all but the first 10\n",
295 |         "loss = history.history['loss']\n",
296 |         "epochs = range(10, len(loss))\n",
297 |         "plot_loss = loss[10:]\n",
298 |         "print(plot_loss)\n",
299 |         "plt.plot(epochs, plot_loss, 'b', label='Training Loss')\n",
300 |         "plt.show()"
301 |       ],
302 |       "execution_count": null,
303 |       "outputs": []
304 |     },
305 |     {
306 |       "cell_type": "code",
307 |       "metadata": {
308 |         "id": "YUOPUeHWvvBG"
309 |       },
310 |       "source": [
311 |         "forecast = []\n",
312 |         "for time in range(len(series) - window_size):\n",
313 |         "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
314 |         "\n",
315 |         "forecast = forecast[split_time-window_size:]\n",
316 |         "results = np.array(forecast)[:, 0, 0]\n",
317 |         "\n",
318 |         "\n",
319 |         "plt.figure(figsize=(10, 6))\n",
320 |         "\n",
321 |         "plot_series(time_valid, x_valid)\n",
322 |         "plot_series(time_valid, results)"
323 |       ],
324 |       "execution_count": null,
325 |       "outputs": []
326 |     },
327 |     {
328 |       "cell_type": "code",
329 |       "metadata": {
330 |         "id": "W-GPjL2wv0yc"
331 |       },
332 |       "source": [
333 |         "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
334 |       ],
335 |       "execution_count": null,
336 |       "outputs": []
337 |     }
338 |   ]
339 | }


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_3_Lesson_2_RNN_with_ts.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "S+P Week 3 Lesson 2 - RNN.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "toc_visible": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "display_name": "Python 3",
 13 |       "name": "python3"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "metadata": {
 20 |         "id": "zX4Kg8DUTKWO"
 21 |       },
 22 |       "source": [
 23 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 24 |         "# you may not use this file except in compliance with the License.\n",
 25 |         "# You may obtain a copy of the License at\n",
 26 |         "#\n",
 27 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 28 |         "#\n",
 29 |         "# Unless required by applicable law or agreed to in writing, software\n",
 30 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 31 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 32 |         "# See the License for the specific language governing permissions and\n",
 33 |         "# limitations under the License."
 34 |       ],
 35 |       "execution_count": null,
 36 |       "outputs": []
 37 |     },
 38 |     {
 39 |       "cell_type": "markdown",
 40 |       "metadata": {
 41 |         "id": "view-in-github"
 42 |       },
 43 |       "source": [
 44 |         "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%203%20Lesson%202%20-%20RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 45 |       ]
 46 |     },
 47 |     {
 48 |       "cell_type": "code",
 49 |       "metadata": {
 50 |         "cellView": "both",
 51 |         "id": "D1J15Vh_1Jih"
 52 |       },
 53 |       "source": [
 54 |         "try:\n",
 55 |         "  # %tensorflow_version only exists in Colab.\n",
 56 |         "  %tensorflow_version 2.x\n",
 57 |         "except Exception:\n",
 58 |         "  pass\n"
 59 |       ],
 60 |       "execution_count": null,
 61 |       "outputs": []
 62 |     },
 63 |     {
 64 |       "cell_type": "code",
 65 |       "metadata": {
 66 |         "id": "BOjujz601HcS"
 67 |       },
 68 |       "source": [
 69 |         "import tensorflow as tf\n",
 70 |         "import numpy as np\n",
 71 |         "import matplotlib.pyplot as plt\n",
 72 |         "print(tf.__version__)"
 73 |       ],
 74 |       "execution_count": null,
 75 |       "outputs": []
 76 |     },
 77 |     {
 78 |       "cell_type": "code",
 79 |       "metadata": {
 80 |         "id": "Zswl7jRtGzkk"
 81 |       },
 82 |       "source": [
 83 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 84 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 85 |         "    plt.xlabel(\"Time\")\n",
 86 |         "    plt.ylabel(\"Value\")\n",
 87 |         "    plt.grid(True)\n",
 88 |         "\n",
 89 |         "def trend(time, slope=0):\n",
 90 |         "    return slope * time\n",
 91 |         "\n",
 92 |         "def seasonal_pattern(season_time):\n",
 93 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
 94 |         "    return np.where(season_time < 0.4,\n",
 95 |         "                    np.cos(season_time * 2 * np.pi),\n",
 96 |         "                    1 / np.exp(3 * season_time))\n",
 97 |         "\n",
 98 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
 99 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
100 |         "    season_time = ((time + phase) % period) / period\n",
101 |         "    return amplitude * seasonal_pattern(season_time)\n",
102 |         "\n",
103 |         "def noise(time, noise_level=1, seed=None):\n",
104 |         "    rnd = np.random.RandomState(seed)\n",
105 |         "    return rnd.randn(len(time)) * noise_level\n",
106 |         "\n",
107 |         "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
108 |         "baseline = 10\n",
109 |         "series = trend(time, 0.1)  \n",
110 |         "baseline = 10\n",
111 |         "amplitude = 40\n",
112 |         "slope = 0.05\n",
113 |         "noise_level = 5\n",
114 |         "\n",
115 |         "# Create the series\n",
116 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
117 |         "# Update with noise\n",
118 |         "series += noise(time, noise_level, seed=42)\n",
119 |         "\n",
120 |         "split_time = 1000\n",
121 |         "time_train = time[:split_time]\n",
122 |         "x_train = series[:split_time]\n",
123 |         "time_valid = time[split_time:]\n",
124 |         "x_valid = series[split_time:]\n",
125 |         "\n",
126 |         "window_size = 20\n",
127 |         "batch_size = 32\n",
128 |         "shuffle_buffer_size = 1000"
129 |       ],
130 |       "execution_count": null,
131 |       "outputs": []
132 |     },
133 |     {
134 |       "cell_type": "code",
135 |       "metadata": {
136 |         "id": "4sTTIOCbyShY"
137 |       },
138 |       "source": [
139 |         "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
140 |         "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
141 |         "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
142 |         "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
143 |         "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
144 |         "  dataset = dataset.batch(batch_size).prefetch(1)\n",
145 |         "  return dataset"
146 |       ],
147 |       "execution_count": null,
148 |       "outputs": []
149 |     },
150 |     {
151 |       "cell_type": "code",
152 |       "metadata": {
153 |         "id": "L4nblWkqg1NL"
154 |       },
155 |       "source": [
156 |         "tf.keras.backend.clear_session()\n",
157 |         "tf.random.set_seed(51)\n",
158 |         "np.random.seed(51)\n",
159 |         "\n",
160 |         "train_set = windowed_dataset(x_train, window_size, batch_size=128, shuffle_buffer=shuffle_buffer_size)\n",
161 |         "\n",
162 |         "model = tf.keras.models.Sequential([\n",
163 |         "  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
164 |         "                      input_shape=[None]),\n",
165 |         "  tf.keras.layers.SimpleRNN(40, return_sequences=True),\n",
166 |         "  tf.keras.layers.SimpleRNN(40),\n",
167 |         "  tf.keras.layers.Dense(1),\n",
168 |         "  tf.keras.layers.Lambda(lambda x: x * 100.0)\n",
169 |         "])\n",
170 |         "\n",
171 |         "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
172 |         "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
173 |         "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
174 |         "model.compile(loss=tf.keras.losses.Huber(),\n",
175 |         "              optimizer=optimizer,\n",
176 |         "              metrics=[\"mae\"])\n",
177 |         "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
178 |       ],
179 |       "execution_count": null,
180 |       "outputs": []
181 |     },
182 |     {
183 |       "cell_type": "code",
184 |       "metadata": {
185 |         "id": "5He3pp-Hj758"
186 |       },
187 |       "source": [
188 |         "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
189 |         "plt.axis([1e-8, 1e-4, 0, 30])"
190 |       ],
191 |       "execution_count": null,
192 |       "outputs": []
193 |     },
194 |     {
195 |       "cell_type": "code",
196 |       "metadata": {
197 |         "id": "6y1KMowRkHkC"
198 |       },
199 |       "source": [
200 |         "tf.keras.backend.clear_session()\n",
201 |         "tf.random.set_seed(51)\n",
202 |         "np.random.seed(51)\n",
203 |         "\n",
204 |         "dataset = windowed_dataset(x_train, window_size, batch_size=128, shuffle_buffer=shuffle_buffer_size)\n",
205 |         "\n",
206 |         "model = tf.keras.models.Sequential([\n",
207 |         "  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
208 |         "                      input_shape=[None]),\n",
209 |         "  tf.keras.layers.SimpleRNN(40, return_sequences=True),\n",
210 |         "  tf.keras.layers.SimpleRNN(40),\n",
211 |         "  tf.keras.layers.Dense(1),\n",
212 |         "  tf.keras.layers.Lambda(lambda x: x * 100.0)\n",
213 |         "])\n",
214 |         "\n",
215 |         "optimizer = tf.keras.optimizers.SGD(lr=5e-5, momentum=0.9)\n",
216 |         "model.compile(loss=tf.keras.losses.Huber(),\n",
217 |         "              optimizer=optimizer,\n",
218 |         "              metrics=[\"mae\"])\n",
219 |         "history = model.fit(dataset,epochs=400)"
220 |       ],
221 |       "execution_count": null,
222 |       "outputs": []
223 |     },
224 |     {
225 |       "cell_type": "code",
226 |       "metadata": {
227 |         "id": "ejBynEKekaKw"
228 |       },
229 |       "source": [
230 |         "forecast=[]\n",
231 |         "for time in range(len(series) - window_size):\n",
232 |         "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
233 |         "\n",
234 |         "forecast = forecast[split_time-window_size:]\n",
235 |         "results = np.array(forecast)[:, 0, 0]\n",
236 |         "\n",
237 |         "\n",
238 |         "plt.figure(figsize=(10, 6))\n",
239 |         "\n",
240 |         "plot_series(time_valid, x_valid)\n",
241 |         "plot_series(time_valid, results)"
242 |       ],
243 |       "execution_count": null,
244 |       "outputs": []
245 |     },
246 |     {
247 |       "cell_type": "code",
248 |       "metadata": {
249 |         "id": "hR2BO0Dai_ZT"
250 |       },
251 |       "source": [
252 |         "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
253 |       ],
254 |       "execution_count": null,
255 |       "outputs": []
256 |     },
257 |     {
258 |       "cell_type": "code",
259 |       "metadata": {
260 |         "id": "vXaFHOaJqE6M"
261 |       },
262 |       "source": [
263 |         "import matplotlib.image  as mpimg\n",
264 |         "import matplotlib.pyplot as plt\n",
265 |         "\n",
266 |         "#-----------------------------------------------------------\n",
267 |         "# Retrieve a list of list results on training and test data\n",
268 |         "# sets for each training epoch\n",
269 |         "#-----------------------------------------------------------\n",
270 |         "mae=history.history['mae']\n",
271 |         "loss=history.history['loss']\n",
272 |         "\n",
273 |         "epochs=range(len(loss)) # Get number of epochs\n",
274 |         "\n",
275 |         "#------------------------------------------------\n",
276 |         "# Plot MAE and Loss\n",
277 |         "#------------------------------------------------\n",
278 |         "plt.plot(epochs, mae, 'r')\n",
279 |         "plt.plot(epochs, loss, 'b')\n",
280 |         "plt.title('MAE and Loss')\n",
281 |         "plt.xlabel(\"Epochs\")\n",
282 |         "plt.ylabel(\"Accuracy\")\n",
283 |         "plt.legend([\"MAE\", \"Loss\"])\n",
284 |         "\n",
285 |         "plt.figure()\n",
286 |         "\n",
287 |         "epochs_zoom = epochs[200:]\n",
288 |         "mae_zoom = mae[200:]\n",
289 |         "loss_zoom = loss[200:]\n",
290 |         "\n",
291 |         "#------------------------------------------------\n",
292 |         "# Plot Zoomed MAE and Loss\n",
293 |         "#------------------------------------------------\n",
294 |         "plt.plot(epochs_zoom, mae_zoom, 'r')\n",
295 |         "plt.plot(epochs_zoom, loss_zoom, 'b')\n",
296 |         "plt.title('MAE and Loss')\n",
297 |         "plt.xlabel(\"Epochs\")\n",
298 |         "plt.ylabel(\"Accuracy\")\n",
299 |         "plt.legend([\"MAE\", \"Loss\"])\n",
300 |         "\n",
301 |         "plt.figure()"
302 |       ],
303 |       "execution_count": null,
304 |       "outputs": []
305 |     }
306 |   ]
307 | }


--------------------------------------------------------------------------------
/SampleCode/S+P_Week_4_Lesson_1.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "S+P Week 4 Lesson 1.ipynb",
  7 |       "provenance": [],
  8 |       "collapsed_sections": [],
  9 |       "toc_visible": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "display_name": "Python 3",
 13 |       "name": "python3"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "metadata": {
 20 |         "id": "zX4Kg8DUTKWO"
 21 |       },
 22 |       "source": [
 23 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 24 |         "# you may not use this file except in compliance with the License.\n",
 25 |         "# You may obtain a copy of the License at\n",
 26 |         "#\n",
 27 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 28 |         "#\n",
 29 |         "# Unless required by applicable law or agreed to in writing, software\n",
 30 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 31 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 32 |         "# See the License for the specific language governing permissions and\n",
 33 |         "# limitations under the License."
 34 |       ],
 35 |       "execution_count": null,
 36 |       "outputs": []
 37 |     },
 38 |     {
 39 |       "cell_type": "markdown",
 40 |       "metadata": {
 41 |         "id": "view-in-github"
 42 |       },
 43 |       "source": [
 44 |         "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%204%20Lesson%201.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 45 |       ]
 46 |     },
 47 |     {
 48 |       "cell_type": "code",
 49 |       "metadata": {
 50 |         "cellView": "both",
 51 |         "id": "D1J15Vh_1Jih"
 52 |       },
 53 |       "source": [
 54 |         "try:\n",
 55 |         "  # %tensorflow_version only exists in Colab.\n",
 56 |         "  %tensorflow_version 2.x\n",
 57 |         "except Exception:\n",
 58 |         "  pass\n"
 59 |       ],
 60 |       "execution_count": null,
 61 |       "outputs": []
 62 |     },
 63 |     {
 64 |       "cell_type": "code",
 65 |       "metadata": {
 66 |         "id": "BOjujz601HcS"
 67 |       },
 68 |       "source": [
 69 |         "import tensorflow as tf\n",
 70 |         "import numpy as np\n",
 71 |         "import matplotlib.pyplot as plt\n",
 72 |         "print(tf.__version__)"
 73 |       ],
 74 |       "execution_count": null,
 75 |       "outputs": []
 76 |     },
 77 |     {
 78 |       "cell_type": "code",
 79 |       "metadata": {
 80 |         "id": "Zswl7jRtGzkk"
 81 |       },
 82 |       "source": [
 83 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 84 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 85 |         "    plt.xlabel(\"Time\")\n",
 86 |         "    plt.ylabel(\"Value\")\n",
 87 |         "    plt.grid(True)\n",
 88 |         "\n",
 89 |         "def trend(time, slope=0):\n",
 90 |         "    return slope * time\n",
 91 |         "\n",
 92 |         "def seasonal_pattern(season_time):\n",
 93 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
 94 |         "    return np.where(season_time < 0.4,\n",
 95 |         "                    np.cos(season_time * 2 * np.pi),\n",
 96 |         "                    1 / np.exp(3 * season_time))\n",
 97 |         "\n",
 98 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
 99 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
100 |         "    season_time = ((time + phase) % period) / period\n",
101 |         "    return amplitude * seasonal_pattern(season_time)\n",
102 |         "\n",
103 |         "def noise(time, noise_level=1, seed=None):\n",
104 |         "    rnd = np.random.RandomState(seed)\n",
105 |         "    return rnd.randn(len(time)) * noise_level\n",
106 |         "\n",
107 |         "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
108 |         "baseline = 10\n",
109 |         "series = trend(time, 0.1)  \n",
110 |         "baseline = 10\n",
111 |         "amplitude = 40\n",
112 |         "slope = 0.05\n",
113 |         "noise_level = 5\n",
114 |         "\n",
115 |         "# Create the series\n",
116 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
117 |         "# Update with noise\n",
118 |         "series += noise(time, noise_level, seed=42)\n",
119 |         "\n",
120 |         "split_time = 1000\n",
121 |         "time_train = time[:split_time]\n",
122 |         "x_train = series[:split_time]\n",
123 |         "time_valid = time[split_time:]\n",
124 |         "x_valid = series[split_time:]\n",
125 |         "\n",
126 |         "window_size = 20\n",
127 |         "batch_size = 32\n",
128 |         "shuffle_buffer_size = 1000"
129 |       ],
130 |       "execution_count": null,
131 |       "outputs": []
132 |     },
133 |     {
134 |       "cell_type": "code",
135 |       "metadata": {
136 |         "id": "4sTTIOCbyShY"
137 |       },
138 |       "source": [
139 |         "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
140 |         "    series = tf.expand_dims(series, axis=-1)\n",
141 |         "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
142 |         "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
143 |         "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
144 |         "    ds = ds.shuffle(shuffle_buffer)\n",
145 |         "    ds = ds.map(lambda w: (w[:-1], w[1:]))\n",
146 |         "    return ds.batch(batch_size).prefetch(1)"
147 |       ],
148 |       "execution_count": null,
149 |       "outputs": []
150 |     },
151 |     {
152 |       "cell_type": "code",
153 |       "metadata": {
154 |         "id": "_eaAX9g_jS5W"
155 |       },
156 |       "source": [
157 |         "def model_forecast(model, series, window_size):\n",
158 |         "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
159 |         "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
160 |         "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
161 |         "    ds = ds.batch(32).prefetch(1)\n",
162 |         "    forecast = model.predict(ds)\n",
163 |         "    return forecast"
164 |       ],
165 |       "execution_count": null,
166 |       "outputs": []
167 |     },
168 |     {
169 |       "cell_type": "code",
170 |       "metadata": {
171 |         "id": "Yqc2GTsps0qf"
172 |       },
173 |       "source": [
174 |         "tf.keras.backend.clear_session()\n",
175 |         "tf.random.set_seed(51)\n",
176 |         "np.random.seed(51)\n",
177 |         "\n",
178 |         "window_size = 30\n",
179 |         "train_set = windowed_dataset(x_train, window_size, batch_size=128, shuffle_buffer=shuffle_buffer_size)\n",
180 |         "\n",
181 |         "model = tf.keras.models.Sequential([\n",
182 |         "  tf.keras.layers.Conv1D(filters=32, kernel_size=5,\n",
183 |         "                      strides=1, padding=\"causal\",\n",
184 |         "                      activation=\"relu\",\n",
185 |         "                      input_shape=[None, 1]),\n",
186 |         "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n",
187 |         "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32, return_sequences=True)),\n",
188 |         "  tf.keras.layers.Dense(1),\n",
189 |         "  tf.keras.layers.Lambda(lambda x: x * 200)\n",
190 |         "])\n",
191 |         "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
192 |         "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
193 |         "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
194 |         "model.compile(loss=tf.keras.losses.Huber(),\n",
195 |         "              optimizer=optimizer,\n",
196 |         "              metrics=[\"mae\"])\n",
197 |         "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
198 |       ],
199 |       "execution_count": null,
200 |       "outputs": []
201 |     },
202 |     {
203 |       "cell_type": "code",
204 |       "metadata": {
205 |         "id": "MjTvASUns0qh"
206 |       },
207 |       "source": [
208 |         "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
209 |         "plt.axis([1e-8, 1e-4, 0, 30])"
210 |       ],
211 |       "execution_count": null,
212 |       "outputs": []
213 |     },
214 |     {
215 |       "cell_type": "code",
216 |       "metadata": {
217 |         "id": "4uh-97bpLZCA"
218 |       },
219 |       "source": [
220 |         "tf.keras.backend.clear_session()\n",
221 |         "tf.random.set_seed(51)\n",
222 |         "np.random.seed(51)\n",
223 |         "#batch_size = 16\n",
224 |         "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
225 |         "\n",
226 |         "model = tf.keras.models.Sequential([\n",
227 |         "  tf.keras.layers.Conv1D(filters=32, kernel_size=3,\n",
228 |         "                      strides=1, padding=\"causal\",\n",
229 |         "                      activation=\"relu\",\n",
230 |         "                      input_shape=[None, 1]),\n",
231 |         "  tf.keras.layers.LSTM(32, return_sequences=True),\n",
232 |         "  tf.keras.layers.LSTM(32, return_sequences=True),\n",
233 |         "  tf.keras.layers.Dense(1),\n",
234 |         "  tf.keras.layers.Lambda(lambda x: x * 200)\n",
235 |         "])\n",
236 |         "\n",
237 |         "optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9)\n",
238 |         "model.compile(loss=tf.keras.losses.Huber(),\n",
239 |         "              optimizer=optimizer,\n",
240 |         "              metrics=[\"mae\"])\n",
241 |         "history = model.fit(dataset,epochs=500)"
242 |       ],
243 |       "execution_count": null,
244 |       "outputs": []
245 |     },
246 |     {
247 |       "cell_type": "code",
248 |       "metadata": {
249 |         "id": "MKkic-mLdkRZ"
250 |       },
251 |       "source": [
252 |         "rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
253 |         "rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]"
254 |       ],
255 |       "execution_count": null,
256 |       "outputs": []
257 |     },
258 |     {
259 |       "cell_type": "code",
260 |       "metadata": {
261 |         "id": "4N1toSetdnQq"
262 |       },
263 |       "source": [
264 |         "plt.figure(figsize=(10, 6))\n",
265 |         "plot_series(time_valid, x_valid)\n",
266 |         "plot_series(time_valid, rnn_forecast)"
267 |       ],
268 |       "execution_count": null,
269 |       "outputs": []
270 |     },
271 |     {
272 |       "cell_type": "code",
273 |       "metadata": {
274 |         "id": "ULKO3JINdqkp"
275 |       },
276 |       "source": [
277 |         "tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()"
278 |       ],
279 |       "execution_count": null,
280 |       "outputs": []
281 |     },
282 |     {
283 |       "cell_type": "code",
284 |       "metadata": {
285 |         "id": "ok8LjNbbkig4"
286 |       },
287 |       "source": [
288 |         "import matplotlib.image  as mpimg\n",
289 |         "import matplotlib.pyplot as plt\n",
290 |         "\n",
291 |         "#-----------------------------------------------------------\n",
292 |         "# Retrieve a list of list results on training and test data\n",
293 |         "# sets for each training epoch\n",
294 |         "#-----------------------------------------------------------\n",
295 |         "mae=history.history['mae']\n",
296 |         "loss=history.history['loss']\n",
297 |         "\n",
298 |         "epochs=range(len(loss)) # Get number of epochs\n",
299 |         "\n",
300 |         "#------------------------------------------------\n",
301 |         "# Plot MAE and Loss\n",
302 |         "#------------------------------------------------\n",
303 |         "plt.plot(epochs, mae, 'r')\n",
304 |         "plt.plot(epochs, loss, 'b')\n",
305 |         "plt.title('MAE and Loss')\n",
306 |         "plt.xlabel(\"Epochs\")\n",
307 |         "plt.ylabel(\"Accuracy\")\n",
308 |         "plt.legend([\"MAE\", \"Loss\"])\n",
309 |         "\n",
310 |         "plt.figure()\n",
311 |         "\n",
312 |         "epochs_zoom = epochs[200:]\n",
313 |         "mae_zoom = mae[200:]\n",
314 |         "loss_zoom = loss[200:]\n",
315 |         "\n",
316 |         "#------------------------------------------------\n",
317 |         "# Plot Zoomed MAE and Loss\n",
318 |         "#------------------------------------------------\n",
319 |         "plt.plot(epochs_zoom, mae_zoom, 'r')\n",
320 |         "plt.plot(epochs_zoom, loss_zoom, 'b')\n",
321 |         "plt.title('MAE and Loss')\n",
322 |         "plt.xlabel(\"Epochs\")\n",
323 |         "plt.ylabel(\"Accuracy\")\n",
324 |         "plt.legend([\"MAE\", \"Loss\"])\n",
325 |         "\n",
326 |         "plt.figure()"
327 |       ],
328 |       "execution_count": null,
329 |       "outputs": []
330 |     }
331 |   ]
332 | }


--------------------------------------------------------------------------------
/SampleCode/Week_1_Exercise_Answer.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "accelerator": "GPU",
  6 |     "colab": {
  7 |       "name": "Week 1 Exercise Answer.ipynb",
  8 |       "provenance": [],
  9 |       "collapsed_sections": [],
 10 |       "toc_visible": true
 11 |     },
 12 |     "kernelspec": {
 13 |       "display_name": "Python 3",
 14 |       "name": "python3"
 15 |     }
 16 |   },
 17 |   "cells": [
 18 |     {
 19 |       "cell_type": "code",
 20 |       "metadata": {
 21 |         "id": "zX4Kg8DUTKWO"
 22 |       },
 23 |       "source": [
 24 |         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 25 |         "# you may not use this file except in compliance with the License.\n",
 26 |         "# You may obtain a copy of the License at\n",
 27 |         "#\n",
 28 |         "# https://www.apache.org/licenses/LICENSE-2.0\n",
 29 |         "#\n",
 30 |         "# Unless required by applicable law or agreed to in writing, software\n",
 31 |         "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 32 |         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 33 |         "# See the License for the specific language governing permissions and\n",
 34 |         "# limitations under the License."
 35 |       ],
 36 |       "execution_count": null,
 37 |       "outputs": []
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "metadata": {
 42 |         "id": "t9HrvPfrSlzS"
 43 |       },
 44 |       "source": [
 45 |         "import tensorflow as tf\n",
 46 |         "print(tf.__version__)\n"
 47 |       ],
 48 |       "execution_count": null,
 49 |       "outputs": []
 50 |     },
 51 |     {
 52 |       "cell_type": "code",
 53 |       "metadata": {
 54 |         "id": "gqWabzlJ63nL"
 55 |       },
 56 |       "source": [
 57 |         "import numpy as np\n",
 58 |         "import matplotlib.pyplot as plt\n",
 59 |         "import tensorflow as tf\n",
 60 |         "from tensorflow import keras\n",
 61 |         "\n",
 62 |         "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
 63 |         "    plt.plot(time[start:end], series[start:end], format)\n",
 64 |         "    plt.xlabel(\"Time\")\n",
 65 |         "    plt.ylabel(\"Value\")\n",
 66 |         "    plt.grid(True)\n",
 67 |         "\n",
 68 |         "def trend(time, slope=0):\n",
 69 |         "    return slope * time\n",
 70 |         "\n",
 71 |         "def seasonal_pattern(season_time):\n",
 72 |         "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
 73 |         "    return np.where(season_time < 0.1,\n",
 74 |         "                    np.cos(season_time * 7 * np.pi),\n",
 75 |         "                    1 / np.exp(5 * season_time))\n",
 76 |         "\n",
 77 |         "def seasonality(time, period, amplitude=1, phase=0):\n",
 78 |         "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
 79 |         "    season_time = ((time + phase) % period) / period\n",
 80 |         "    return amplitude * seasonal_pattern(season_time)\n",
 81 |         "\n",
 82 |         "def noise(time, noise_level=1, seed=None):\n",
 83 |         "    rnd = np.random.RandomState(seed)\n",
 84 |         "    return rnd.randn(len(time)) * noise_level\n",
 85 |         "\n",
 86 |         "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
 87 |         "baseline = 10\n",
 88 |         "series = trend(time, 0.1)  \n",
 89 |         "baseline = 10\n",
 90 |         "amplitude = 40\n",
 91 |         "slope = 0.01\n",
 92 |         "noise_level = 2\n",
 93 |         "\n",
 94 |         "# Create the series\n",
 95 |         "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
 96 |         "# Update with noise\n",
 97 |         "series += noise(time, noise_level, seed=42)\n",
 98 |         "\n",
 99 |         "plt.figure(figsize=(10, 6))\n",
100 |         "plot_series(time, series)\n",
101 |         "plt.show()"
102 |       ],
103 |       "execution_count": null,
104 |       "outputs": []
105 |     },
106 |     {
107 |       "cell_type": "markdown",
108 |       "metadata": {
109 |         "id": "UfdyqJJ1VZVu"
110 |       },
111 |       "source": [
112 |         "Now that we have the time series, let's split it so we can start forecasting"
113 |       ]
114 |     },
115 |     {
116 |       "cell_type": "code",
117 |       "metadata": {
118 |         "id": "_w0eKap5uFNP"
119 |       },
120 |       "source": [
121 |         "split_time = 1100\n",
122 |         "time_train = time[:split_time]\n",
123 |         "x_train = series[:split_time]\n",
124 |         "time_valid = time[split_time:]\n",
125 |         "x_valid = series[split_time:]\n",
126 |         "plt.figure(figsize=(10, 6))\n",
127 |         "plot_series(time_train, x_train)\n",
128 |         "plt.show()\n",
129 |         "\n",
130 |         "plt.figure(figsize=(10, 6))\n",
131 |         "plot_series(time_valid, x_valid)\n",
132 |         "plt.show()"
133 |       ],
134 |       "execution_count": null,
135 |       "outputs": []
136 |     },
137 |     {
138 |       "cell_type": "markdown",
139 |       "metadata": {
140 |         "id": "bjD8ncEZbjEW"
141 |       },
142 |       "source": [
143 |         "# Naive Forecast"
144 |       ]
145 |     },
146 |     {
147 |       "cell_type": "code",
148 |       "metadata": {
149 |         "id": "Pj_-uCeYxcAb"
150 |       },
151 |       "source": [
152 |         "naive_forecast = series[split_time - 1:-1]"
153 |       ],
154 |       "execution_count": null,
155 |       "outputs": []
156 |     },
157 |     {
158 |       "cell_type": "code",
159 |       "metadata": {
160 |         "id": "JtxwHj9Ig0jT"
161 |       },
162 |       "source": [
163 |         "plt.figure(figsize=(10, 6))\n",
164 |         "plot_series(time_valid, x_valid)\n",
165 |         "plot_series(time_valid, naive_forecast)"
166 |       ],
167 |       "execution_count": null,
168 |       "outputs": []
169 |     },
170 |     {
171 |       "cell_type": "markdown",
172 |       "metadata": {
173 |         "id": "fw1SP5WeuixH"
174 |       },
175 |       "source": [
176 |         "Let's zoom in on the start of the validation period:"
177 |       ]
178 |     },
179 |     {
180 |       "cell_type": "code",
181 |       "metadata": {
182 |         "id": "D0MKg7FNug9V"
183 |       },
184 |       "source": [
185 |         "plt.figure(figsize=(10, 6))\n",
186 |         "plot_series(time_valid, x_valid, start=0, end=150)\n",
187 |         "plot_series(time_valid, naive_forecast, start=1, end=151)"
188 |       ],
189 |       "execution_count": null,
190 |       "outputs": []
191 |     },
192 |     {
193 |       "cell_type": "markdown",
194 |       "metadata": {
195 |         "id": "35gIlQLfu0TT"
196 |       },
197 |       "source": [
198 |         "You can see that the naive forecast lags 1 step behind the time series."
199 |       ]
200 |     },
201 |     {
202 |       "cell_type": "markdown",
203 |       "metadata": {
204 |         "id": "Uh_7244Gsxfx"
205 |       },
206 |       "source": [
207 |         "Now let's compute the mean squared error and the mean absolute error between the forecasts and the predictions in the validation period:"
208 |       ]
209 |     },
210 |     {
211 |       "cell_type": "code",
212 |       "metadata": {
213 |         "id": "byNnC7IbsnMZ"
214 |       },
215 |       "source": [
216 |         "print(keras.metrics.mean_squared_error(x_valid, naive_forecast).numpy())\n",
217 |         "print(keras.metrics.mean_absolute_error(x_valid, naive_forecast).numpy())"
218 |       ],
219 |       "execution_count": null,
220 |       "outputs": []
221 |     },
222 |     {
223 |       "cell_type": "markdown",
224 |       "metadata": {
225 |         "id": "WGPBC9QttI1u"
226 |       },
227 |       "source": [
228 |         "That's our baseline, now let's try a moving average:"
229 |       ]
230 |     },
231 |     {
232 |       "cell_type": "code",
233 |       "metadata": {
234 |         "id": "YGz5UsUdf2tV"
235 |       },
236 |       "source": [
237 |         "def moving_average_forecast(series, window_size):\n",
238 |         "  \"\"\"Forecasts the mean of the last few values.\n",
239 |         "     If window_size=1, then this is equivalent to naive forecast\"\"\"\n",
240 |         "  forecast = []\n",
241 |         "  for time in range(len(series) - window_size):\n",
242 |         "    forecast.append(series[time:time + window_size].mean())\n",
243 |         "  return np.array(forecast)"
244 |       ],
245 |       "execution_count": null,
246 |       "outputs": []
247 |     },
248 |     {
249 |       "cell_type": "code",
250 |       "metadata": {
251 |         "id": "HHFhGXQji7_r"
252 |       },
253 |       "source": [
254 |         "moving_avg = moving_average_forecast(series, 30)[split_time - 30:]\n",
255 |         "\n",
256 |         "plt.figure(figsize=(10, 6))\n",
257 |         "plot_series(time_valid, x_valid)\n",
258 |         "plot_series(time_valid, moving_avg)"
259 |       ],
260 |       "execution_count": null,
261 |       "outputs": []
262 |     },
263 |     {
264 |       "cell_type": "code",
265 |       "metadata": {
266 |         "id": "wG7pTAd7z0e8"
267 |       },
268 |       "source": [
269 |         "print(keras.metrics.mean_squared_error(x_valid, moving_avg).numpy())\n",
270 |         "print(keras.metrics.mean_absolute_error(x_valid, moving_avg).numpy())"
271 |       ],
272 |       "execution_count": null,
273 |       "outputs": []
274 |     },
275 |     {
276 |       "cell_type": "markdown",
277 |       "metadata": {
278 |         "id": "JMYPnJqwz8nS"
279 |       },
280 |       "source": [
281 |         "That's worse than naive forecast! The moving average does not anticipate trend or seasonality, so let's try to remove them by using differencing. Since the seasonality period is 365 days, we will subtract the value at time *t* – 365 from the value at time *t*."
282 |       ]
283 |     },
284 |     {
285 |       "cell_type": "code",
286 |       "metadata": {
287 |         "id": "5pqySF7-rJR4"
288 |       },
289 |       "source": [
290 |         "diff_series = (series[365:] - series[:-365])\n",
291 |         "diff_time = time[365:]\n",
292 |         "\n",
293 |         "plt.figure(figsize=(10, 6))\n",
294 |         "plot_series(diff_time, diff_series)\n",
295 |         "plt.show()"
296 |       ],
297 |       "execution_count": null,
298 |       "outputs": []
299 |     },
300 |     {
301 |       "cell_type": "markdown",
302 |       "metadata": {
303 |         "id": "xPlPlS7DskWg"
304 |       },
305 |       "source": [
306 |         "Great, the trend and seasonality seem to be gone, so now we can use the moving average:"
307 |       ]
308 |     },
309 |     {
310 |       "cell_type": "code",
311 |       "metadata": {
312 |         "id": "QmZpz7arsjbb"
313 |       },
314 |       "source": [
315 |         "diff_moving_avg = moving_average_forecast(diff_series, 50)[split_time - 365 - 50:]\n",
316 |         "\n",
317 |         "plt.figure(figsize=(10, 6))\n",
318 |         "plot_series(time_valid, diff_series[split_time - 365:])\n",
319 |         "plot_series(time_valid, diff_moving_avg)\n",
320 |         "plt.show()"
321 |       ],
322 |       "execution_count": null,
323 |       "outputs": []
324 |     },
325 |     {
326 |       "cell_type": "markdown",
327 |       "metadata": {
328 |         "id": "Gno9S2lyecnc"
329 |       },
330 |       "source": [
331 |         "Now let's bring back the trend and seasonality by adding the past values from t – 365:"
332 |       ]
333 |     },
334 |     {
335 |       "cell_type": "code",
336 |       "metadata": {
337 |         "id": "Dv6RWFq7TFGB"
338 |       },
339 |       "source": [
340 |         "diff_moving_avg_plus_past = series[split_time - 365:-365] + diff_moving_avg\n",
341 |         "\n",
342 |         "plt.figure(figsize=(10, 6))\n",
343 |         "plot_series(time_valid, x_valid)\n",
344 |         "plot_series(time_valid, diff_moving_avg_plus_past)\n",
345 |         "plt.show()"
346 |       ],
347 |       "execution_count": null,
348 |       "outputs": []
349 |     },
350 |     {
351 |       "cell_type": "code",
352 |       "metadata": {
353 |         "id": "59jmBrwcTFCx"
354 |       },
355 |       "source": [
356 |         "print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_past).numpy())\n",
357 |         "print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_past).numpy())"
358 |       ],
359 |       "execution_count": null,
360 |       "outputs": []
361 |     },
362 |     {
363 |       "cell_type": "markdown",
364 |       "metadata": {
365 |         "id": "vx9Et1Hkeusl"
366 |       },
367 |       "source": [
368 |         "Better than naive forecast, good. However the forecasts look a bit too random, because we're just adding past values, which were noisy. Let's use a moving averaging on past values to remove some of the noise:"
369 |       ]
370 |     },
371 |     {
372 |       "cell_type": "code",
373 |       "metadata": {
374 |         "id": "K81dtROoTE_r"
375 |       },
376 |       "source": [
377 |         "diff_moving_avg_plus_smooth_past = moving_average_forecast(series[split_time - 370:-360], 10) + diff_moving_avg\n",
378 |         "\n",
379 |         "plt.figure(figsize=(10, 6))\n",
380 |         "plot_series(time_valid, x_valid)\n",
381 |         "plot_series(time_valid, diff_moving_avg_plus_smooth_past)\n",
382 |         "plt.show()"
383 |       ],
384 |       "execution_count": null,
385 |       "outputs": []
386 |     },
387 |     {
388 |       "cell_type": "code",
389 |       "metadata": {
390 |         "id": "iN2MsBxWTE3m"
391 |       },
392 |       "source": [
393 |         "print(keras.metrics.mean_squared_error(x_valid, diff_moving_avg_plus_smooth_past).numpy())\n",
394 |         "print(keras.metrics.mean_absolute_error(x_valid, diff_moving_avg_plus_smooth_past).numpy())"
395 |       ],
396 |       "execution_count": null,
397 |       "outputs": []
398 |     }
399 |   ]
400 | }


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