├── README.md └── UnivariateTimeSeries.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Time-Series-Forecasting -------------------------------------------------------------------------------- /UnivariateTimeSeries.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### Develop LSTM Models For Univariate Time Series Forecasting" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 48, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "\n", 17 | "# univariate lstm example\n", 18 | "import numpy as np\n", 19 | "from tensorflow.keras.models import Sequential\n", 20 | "from tensorflow.keras.layers import LSTM\n", 21 | "from tensorflow.keras.layers import Dense\n", 22 | "from tensorflow.keras.layers import Flatten\n" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 92, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "# preparing independent and dependent features\n", 32 | "def prepare_data(timeseries_data, n_features):\n", 33 | "\tX, y =[],[]\n", 34 | "\tfor i in range(len(timeseries_data)):\n", 35 | "\t\t# find the end of this pattern\n", 36 | "\t\tend_ix = i + n_features\n", 37 | "\t\t# check if we are beyond the sequence\n", 38 | "\t\tif end_ix > len(timeseries_data)-1:\n", 39 | "\t\t\tbreak\n", 40 | "\t\t# gather input and output parts of the pattern\n", 41 | "\t\tseq_x, seq_y = timeseries_data[i:end_ix], timeseries_data[end_ix]\n", 42 | "\t\tX.append(seq_x)\n", 43 | "\t\ty.append(seq_y)\n", 44 | "\treturn np.array(X), np.array(y)" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 93, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "# define input sequence\n", 54 | "timeseries_data = [110, 125, 133, 146, 158, 172, 187, 196, 210]\n", 55 | "# choose a number of time steps\n", 56 | "n_steps = 3\n", 57 | "# split into samples\n", 58 | "X, y = prepare_data(timeseries_data, n_steps)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 94, 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "[[110 125 133]\n", 71 | " [125 133 146]\n", 72 | " [133 146 158]\n", 73 | " [146 158 172]\n", 74 | " [158 172 187]\n", 75 | " [172 187 196]]\n", 76 | "[146 158 172 187 196 210]\n" 77 | ] 78 | }, 79 | { 80 | "data": { 81 | "text/plain": [ 82 | "(None, None)" 83 | ] 84 | }, 85 | "execution_count": 94, 86 | "metadata": {}, 87 | "output_type": "execute_result" 88 | } 89 | ], 90 | "source": [ 91 | "print(X),print(y)" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 96, 97 | "metadata": {}, 98 | "outputs": [ 99 | { 100 | "data": { 101 | "text/plain": [ 102 | "(6, 3, 1)" 103 | ] 104 | }, 105 | "execution_count": 96, 106 | "metadata": {}, 107 | "output_type": "execute_result" 108 | } 109 | ], 110 | "source": [ 111 | "X.shape" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 95, 117 | "metadata": {}, 118 | "outputs": [], 119 | "source": [ 120 | "# reshape from [samples, timesteps] into [samples, timesteps, features]\n", 121 | "n_features = 1\n", 122 | "X = X.reshape((X.shape[0], X.shape[1], n_features))" 123 | ] 124 | }, 125 | { 126 | "cell_type": "markdown", 127 | "metadata": {}, 128 | "source": [ 129 | "### Building LSTM Model" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": 98, 135 | "metadata": {}, 136 | "outputs": [ 137 | { 138 | "name": "stdout", 139 | "output_type": "stream", 140 | "text": [ 141 | "Train on 6 samples\n", 142 | "Epoch 1/300\n", 143 | "6/6 [==============================] - 4s 630ms/sample - loss: 28792.3184\n", 144 | "Epoch 2/300\n", 145 | "6/6 [==============================] - 0s 2ms/sample - loss: 28158.4785\n", 146 | "Epoch 3/300\n", 147 | "6/6 [==============================] - 0s 2ms/sample - loss: 27535.7031\n", 148 | "Epoch 4/300\n", 149 | "6/6 [==============================] - 0s 2ms/sample - loss: 26879.9277\n", 150 | "Epoch 5/300\n", 151 | "6/6 [==============================] - 0s 2ms/sample - loss: 26205.4238\n", 152 | "Epoch 6/300\n", 153 | "6/6 [==============================] - 0s 2ms/sample - loss: 25542.6230\n", 154 | "Epoch 7/300\n", 155 | "6/6 [==============================] - 0s 2ms/sample - loss: 24897.1934\n", 156 | "Epoch 8/300\n", 157 | "6/6 [==============================] - 0s 2ms/sample - loss: 24219.8691\n", 158 | "Epoch 9/300\n", 159 | "6/6 [==============================] - 0s 2ms/sample - loss: 23500.6191\n", 160 | "Epoch 10/300\n", 161 | "6/6 [==============================] - 0s 2ms/sample - loss: 22745.9512\n", 162 | "Epoch 11/300\n", 163 | "6/6 [==============================] - 0s 2ms/sample - loss: 21970.0059\n", 164 | "Epoch 12/300\n", 165 | "6/6 [==============================] - 0s 2ms/sample - loss: 21168.3633\n", 166 | "Epoch 13/300\n", 167 | "6/6 [==============================] - 0s 2ms/sample - loss: 20330.2637\n", 168 | "Epoch 14/300\n", 169 | "6/6 [==============================] - 0s 2ms/sample - loss: 19453.4551\n", 170 | "Epoch 15/300\n", 171 | "6/6 [==============================] - 0s 2ms/sample - loss: 18530.8887\n", 172 | "Epoch 16/300\n", 173 | "6/6 [==============================] - 0s 2ms/sample - loss: 17550.5137\n", 174 | "Epoch 17/300\n", 175 | "6/6 [==============================] - 0s 2ms/sample - loss: 16522.0137\n", 176 | "Epoch 18/300\n", 177 | "6/6 [==============================] - 0s 2ms/sample - loss: 15427.3545\n", 178 | "Epoch 19/300\n", 179 | "6/6 [==============================] - 0s 3ms/sample - loss: 14261.7373\n", 180 | "Epoch 20/300\n", 181 | "6/6 [==============================] - 0s 3ms/sample - loss: 13017.2451\n", 182 | "Epoch 21/300\n", 183 | "6/6 [==============================] - 0s 4ms/sample - loss: 11698.5967\n", 184 | "Epoch 22/300\n", 185 | "6/6 [==============================] - 0s 4ms/sample - loss: 10306.4404\n", 186 | "Epoch 23/300\n", 187 | "6/6 [==============================] - 0s 4ms/sample - loss: 8856.3545\n", 188 | "Epoch 24/300\n", 189 | "6/6 [==============================] - 0s 4ms/sample - loss: 7433.4761\n", 190 | "Epoch 25/300\n", 191 | "6/6 [==============================] - 0s 4ms/sample - loss: 6156.0562\n", 192 | "Epoch 26/300\n", 193 | "6/6 [==============================] - 0s 4ms/sample - loss: 5036.9307\n", 194 | "Epoch 27/300\n", 195 | "6/6 [==============================] - 0s 4ms/sample - loss: 4013.8164\n", 196 | "Epoch 28/300\n", 197 | "6/6 [==============================] - 0s 4ms/sample - loss: 3044.9094\n", 198 | "Epoch 29/300\n", 199 | "6/6 [==============================] - 0s 4ms/sample - loss: 2121.4495\n", 200 | "Epoch 30/300\n", 201 | "6/6 [==============================] - 0s 4ms/sample - loss: 1256.9219\n", 202 | "Epoch 31/300\n", 203 | "6/6 [==============================] - 0s 4ms/sample - loss: 552.7951\n", 204 | "Epoch 32/300\n", 205 | "6/6 [==============================] - 0s 4ms/sample - loss: 127.3624\n", 206 | "Epoch 33/300\n", 207 | "6/6 [==============================] - 0s 5ms/sample - loss: 20.5076\n", 208 | "Epoch 34/300\n", 209 | "6/6 [==============================] - 0s 4ms/sample - loss: 330.2190\n", 210 | "Epoch 35/300\n", 211 | "6/6 [==============================] - 0s 4ms/sample - loss: 651.8246\n", 212 | "Epoch 36/300\n", 213 | "6/6 [==============================] - 0s 4ms/sample - loss: 723.1823\n", 214 | "Epoch 37/300\n", 215 | "6/6 [==============================] - 0s 4ms/sample - loss: 606.5361\n", 216 | "Epoch 38/300\n", 217 | "6/6 [==============================] - 0s 3ms/sample - loss: 473.7505\n", 218 | "Epoch 39/300\n", 219 | "6/6 [==============================] - 0s 4ms/sample - loss: 356.8016\n", 220 | "Epoch 40/300\n", 221 | "6/6 [==============================] - 0s 4ms/sample - loss: 246.6154\n", 222 | "Epoch 41/300\n", 223 | "6/6 [==============================] - 0s 4ms/sample - loss: 153.6763\n", 224 | "Epoch 42/300\n", 225 | "6/6 [==============================] - 0s 4ms/sample - loss: 87.5187\n", 226 | "Epoch 43/300\n", 227 | "6/6 [==============================] - 0s 3ms/sample - loss: 45.8756\n", 228 | "Epoch 44/300\n", 229 | "6/6 [==============================] - 0s 4ms/sample - loss: 22.8299\n", 230 | "Epoch 45/300\n", 231 | "6/6 [==============================] - 0s 4ms/sample - loss: 13.7281\n", 232 | "Epoch 46/300\n", 233 | "6/6 [==============================] - 0s 4ms/sample - loss: 14.8358\n", 234 | "Epoch 47/300\n", 235 | "6/6 [==============================] - 0s 4ms/sample - loss: 22.4987\n", 236 | "Epoch 48/300\n", 237 | "6/6 [==============================] - 0s 4ms/sample - loss: 32.9924\n", 238 | "Epoch 49/300\n", 239 | "6/6 [==============================] - 0s 3ms/sample - loss: 43.2194\n", 240 | "Epoch 50/300\n", 241 | "6/6 [==============================] - 0s 3ms/sample - loss: 51.0727\n", 242 | "Epoch 51/300\n", 243 | "6/6 [==============================] - 0s 3ms/sample - loss: 55.3907\n", 244 | "Epoch 52/300\n", 245 | "6/6 [==============================] - 0s 3ms/sample - loss: 55.8399\n", 246 | "Epoch 53/300\n", 247 | "6/6 [==============================] - 0s 3ms/sample - loss: 52.7841\n", 248 | "Epoch 54/300\n", 249 | "6/6 [==============================] - 0s 3ms/sample - loss: 47.0850\n", 250 | "Epoch 55/300\n", 251 | "6/6 [==============================] - 0s 3ms/sample - loss: 39.8269\n", 252 | "Epoch 56/300\n", 253 | "6/6 [==============================] - 0s 3ms/sample - loss: 32.0760\n", 254 | "Epoch 57/300\n", 255 | "6/6 [==============================] - 0s 3ms/sample - loss: 24.7542\n", 256 | "Epoch 58/300\n", 257 | "6/6 [==============================] - 0s 3ms/sample - loss: 18.6409\n", 258 | "Epoch 59/300\n", 259 | "6/6 [==============================] - 0s 3ms/sample - loss: 14.4606\n", 260 | "Epoch 60/300\n", 261 | "6/6 [==============================] - 0s 3ms/sample - loss: 13.0567\n", 262 | "Epoch 61/300\n", 263 | "6/6 [==============================] - 0s 2ms/sample - loss: 14.6700\n", 264 | "Epoch 62/300\n", 265 | "6/6 [==============================] - 0s 4ms/sample - loss: 15.5140\n", 266 | "Epoch 63/300\n", 267 | "6/6 [==============================] - 0s 2ms/sample - loss: 13.5926\n", 268 | "Epoch 64/300\n", 269 | "6/6 [==============================] - 0s 3ms/sample - loss: 10.9403\n", 270 | "Epoch 65/300\n", 271 | "6/6 [==============================] - 0s 2ms/sample - loss: 10.2498\n", 272 | "Epoch 66/300\n", 273 | "6/6 [==============================] - 0s 2ms/sample - loss: 10.4810\n", 274 | "Epoch 67/300\n", 275 | "6/6 [==============================] - 0s 2ms/sample - loss: 10.6402\n", 276 | "Epoch 68/300\n", 277 | "6/6 [==============================] - 0s 2ms/sample - loss: 10.6266\n", 278 | "Epoch 69/300\n", 279 | "6/6 [==============================] - 0s 4ms/sample - loss: 10.9874\n", 280 | "Epoch 70/300\n", 281 | "6/6 [==============================] - 0s 4ms/sample - loss: 10.8149\n", 282 | "Epoch 71/300\n", 283 | "6/6 [==============================] - 0s 3ms/sample - loss: 10.6275\n", 284 | "Epoch 72/300\n", 285 | "6/6 [==============================] - 0s 3ms/sample - loss: 10.6994\n", 286 | "Epoch 73/300\n", 287 | "6/6 [==============================] - 0s 4ms/sample - loss: 10.7079\n", 288 | "Epoch 74/300\n", 289 | "6/6 [==============================] - 0s 4ms/sample - loss: 10.4804\n", 290 | "Epoch 75/300\n", 291 | "6/6 [==============================] - 0s 4ms/sample - loss: 10.2964\n", 292 | "Epoch 76/300\n", 293 | "6/6 [==============================] - 0s 4ms/sample - loss: 10.2627\n", 294 | "Epoch 77/300\n", 295 | "6/6 [==============================] - 0s 3ms/sample - loss: 10.0290\n", 296 | "Epoch 78/300\n", 297 | "6/6 [==============================] - 0s 2ms/sample - loss: 9.8037\n", 298 | "Epoch 79/300\n", 299 | "6/6 [==============================] - 0s 2ms/sample - loss: 9.6798\n", 300 | "Epoch 80/300\n", 301 | "6/6 [==============================] - 0s 3ms/sample - loss: 9.4906\n", 302 | "Epoch 81/300\n", 303 | "6/6 [==============================] - 0s 4ms/sample - loss: 9.3321\n", 304 | "Epoch 82/300\n", 305 | "6/6 [==============================] - 0s 4ms/sample - loss: 9.0549\n", 306 | "Epoch 83/300\n", 307 | "6/6 [==============================] - 0s 3ms/sample - loss: 8.7834\n", 308 | "Epoch 84/300\n", 309 | "6/6 [==============================] - 0s 4ms/sample - loss: 8.5207\n", 310 | "Epoch 85/300\n", 311 | "6/6 [==============================] - 0s 4ms/sample - loss: 8.2323\n", 312 | "Epoch 86/300\n", 313 | "6/6 [==============================] - 0s 4ms/sample - loss: 7.8906\n", 314 | "Epoch 87/300\n", 315 | "6/6 [==============================] - 0s 3ms/sample - loss: 7.4830\n", 316 | "Epoch 88/300\n", 317 | "6/6 [==============================] - 0s 4ms/sample - loss: 6.9968\n", 318 | "Epoch 89/300\n", 319 | "6/6 [==============================] - 0s 4ms/sample - loss: 6.4655\n", 320 | "Epoch 90/300\n", 321 | "6/6 [==============================] - 0s 4ms/sample - loss: 5.8933\n", 322 | "Epoch 91/300\n", 323 | "6/6 [==============================] - 0s 3ms/sample - loss: 5.2149\n", 324 | "Epoch 92/300\n", 325 | "6/6 [==============================] - 0s 3ms/sample - loss: 4.4812\n", 326 | "Epoch 93/300\n", 327 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.8566\n", 328 | "Epoch 94/300\n", 329 | "6/6 [==============================] - 0s 4ms/sample - loss: 3.3742\n", 330 | "Epoch 95/300\n", 331 | "6/6 [==============================] - 0s 4ms/sample - loss: 3.1039\n", 332 | "Epoch 96/300\n", 333 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.1335\n", 334 | "Epoch 97/300\n", 335 | "6/6 [==============================] - 0s 2ms/sample - loss: 4.1070\n", 336 | "Epoch 98/300\n", 337 | "6/6 [==============================] - 0s 3ms/sample - loss: 4.9459\n", 338 | "Epoch 99/300\n", 339 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.3435\n" 340 | ] 341 | }, 342 | { 343 | "name": "stdout", 344 | "output_type": "stream", 345 | "text": [ 346 | "Epoch 100/300\n", 347 | "6/6 [==============================] - 0s 3ms/sample - loss: 5.2086\n", 348 | "Epoch 101/300\n", 349 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.9660\n", 350 | "Epoch 102/300\n", 351 | "6/6 [==============================] - 0s 3ms/sample - loss: 4.4694\n", 352 | "Epoch 103/300\n", 353 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.3676\n", 354 | "Epoch 104/300\n", 355 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.7143\n", 356 | "Epoch 105/300\n", 357 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.6114\n", 358 | "Epoch 106/300\n", 359 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.1115\n", 360 | "Epoch 107/300\n", 361 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.7512\n", 362 | "Epoch 108/300\n", 363 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.9237\n", 364 | "Epoch 109/300\n", 365 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.4108\n", 366 | "Epoch 110/300\n", 367 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.1767\n", 368 | "Epoch 111/300\n", 369 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.9337\n", 370 | "Epoch 112/300\n", 371 | "6/6 [==============================] - 0s 3ms/sample - loss: 3.3078\n", 372 | "Epoch 113/300\n", 373 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.8605\n", 374 | "Epoch 114/300\n", 375 | "6/6 [==============================] - 0s 2ms/sample - loss: 3.0622\n", 376 | "Epoch 115/300\n", 377 | "6/6 [==============================] - 0s 2ms/sample - loss: 3.0393\n", 378 | "Epoch 116/300\n", 379 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7974\n", 380 | "Epoch 117/300\n", 381 | "6/6 [==============================] - 0s 2ms/sample - loss: 3.0493\n", 382 | "Epoch 118/300\n", 383 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7950\n", 384 | "Epoch 119/300\n", 385 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.8544\n", 386 | "Epoch 120/300\n", 387 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.8935\n", 388 | "Epoch 121/300\n", 389 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7146\n", 390 | "Epoch 122/300\n", 391 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.8813\n", 392 | "Epoch 123/300\n", 393 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7191\n", 394 | "Epoch 124/300\n", 395 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7745\n", 396 | "Epoch 125/300\n", 397 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7688\n", 398 | "Epoch 126/300\n", 399 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.6841\n", 400 | "Epoch 127/300\n", 401 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.7739\n", 402 | "Epoch 128/300\n", 403 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.6543\n", 404 | "Epoch 129/300\n", 405 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.7269\n", 406 | "Epoch 130/300\n", 407 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.6654\n", 408 | "Epoch 131/300\n", 409 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.6627\n", 410 | "Epoch 132/300\n", 411 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.6768\n", 412 | "Epoch 133/300\n", 413 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.6186\n", 414 | "Epoch 134/300\n", 415 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.6686\n", 416 | "Epoch 135/300\n", 417 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.6039\n", 418 | "Epoch 136/300\n", 419 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.6412\n", 420 | "Epoch 137/300\n", 421 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.6052\n", 422 | "Epoch 138/300\n", 423 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.6133\n", 424 | "Epoch 139/300\n", 425 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.6081\n", 426 | "Epoch 140/300\n", 427 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.5917\n", 428 | "Epoch 141/300\n", 429 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.6060\n", 430 | "Epoch 142/300\n", 431 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.5782\n", 432 | "Epoch 143/300\n", 433 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5988\n", 434 | "Epoch 144/300\n", 435 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5706\n", 436 | "Epoch 145/300\n", 437 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.5870\n", 438 | "Epoch 146/300\n", 439 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5652\n", 440 | "Epoch 147/300\n", 441 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5748\n", 442 | "Epoch 148/300\n", 443 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5593\n", 444 | "Epoch 149/300\n", 445 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.5624\n", 446 | "Epoch 150/300\n", 447 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5526\n", 448 | "Epoch 151/300\n", 449 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5512\n", 450 | "Epoch 152/300\n", 451 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.5446\n", 452 | "Epoch 153/300\n", 453 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5407\n", 454 | "Epoch 154/300\n", 455 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5358\n", 456 | "Epoch 155/300\n", 457 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5308\n", 458 | "Epoch 156/300\n", 459 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5259\n", 460 | "Epoch 157/300\n", 461 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5205\n", 462 | "Epoch 158/300\n", 463 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5144\n", 464 | "Epoch 159/300\n", 465 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5084\n", 466 | "Epoch 160/300\n", 467 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.5001\n", 468 | "Epoch 161/300\n", 469 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4964\n", 470 | "Epoch 162/300\n", 471 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4854\n", 472 | "Epoch 163/300\n", 473 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4817\n", 474 | "Epoch 164/300\n", 475 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4673\n", 476 | "Epoch 165/300\n", 477 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4569\n", 478 | "Epoch 166/300\n", 479 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.4463\n", 480 | "Epoch 167/300\n", 481 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4282\n", 482 | "Epoch 168/300\n", 483 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4113\n", 484 | "Epoch 169/300\n", 485 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.3979\n", 486 | "Epoch 170/300\n", 487 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4531\n", 488 | "Epoch 171/300\n", 489 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.5575\n", 490 | "Epoch 172/300\n", 491 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4304\n", 492 | "Epoch 173/300\n", 493 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.3901\n", 494 | "Epoch 174/300\n", 495 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.4893\n", 496 | "Epoch 175/300\n", 497 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.3478\n", 498 | "Epoch 176/300\n", 499 | "6/6 [==============================] - 0s 3ms/sample - loss: 2.3975\n", 500 | "Epoch 177/300\n", 501 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.4407\n", 502 | "Epoch 178/300\n", 503 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.3141\n", 504 | "Epoch 179/300\n", 505 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.3750\n", 506 | "Epoch 180/300\n", 507 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.3776\n", 508 | "Epoch 181/300\n", 509 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2901\n", 510 | "Epoch 182/300\n", 511 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.3572\n", 512 | "Epoch 183/300\n", 513 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.3103\n", 514 | "Epoch 184/300\n", 515 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2651\n", 516 | "Epoch 185/300\n", 517 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.3297\n", 518 | "Epoch 186/300\n", 519 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2949\n", 520 | "Epoch 187/300\n", 521 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2282\n", 522 | "Epoch 188/300\n", 523 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2732\n", 524 | "Epoch 189/300\n", 525 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2644\n", 526 | "Epoch 190/300\n", 527 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1964\n", 528 | "Epoch 191/300\n", 529 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2199\n", 530 | "Epoch 192/300\n", 531 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2519\n", 532 | "Epoch 193/300\n", 533 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1956\n", 534 | "Epoch 194/300\n", 535 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1444\n", 536 | "Epoch 195/300\n", 537 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1525\n", 538 | "Epoch 196/300\n", 539 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1730\n", 540 | "Epoch 197/300\n", 541 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1666\n", 542 | "Epoch 198/300\n", 543 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1182\n", 544 | "Epoch 199/300\n", 545 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0773\n" 546 | ] 547 | }, 548 | { 549 | "name": "stdout", 550 | "output_type": "stream", 551 | "text": [ 552 | "Epoch 200/300\n", 553 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0562\n", 554 | "Epoch 201/300\n", 555 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0545\n", 556 | "Epoch 202/300\n", 557 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0880\n", 558 | "Epoch 203/300\n", 559 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1988\n", 560 | "Epoch 204/300\n", 561 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.4486\n", 562 | "Epoch 205/300\n", 563 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2860\n", 564 | "Epoch 206/300\n", 565 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9903\n", 566 | "Epoch 207/300\n", 567 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1246\n", 568 | "Epoch 208/300\n", 569 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.2679\n", 570 | "Epoch 209/300\n", 571 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0778\n", 572 | "Epoch 210/300\n", 573 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9488\n", 574 | "Epoch 211/300\n", 575 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.1291\n", 576 | "Epoch 212/300\n", 577 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9911\n", 578 | "Epoch 213/300\n", 579 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9465\n", 580 | "Epoch 214/300\n", 581 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0914\n", 582 | "Epoch 215/300\n", 583 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9536\n", 584 | "Epoch 216/300\n", 585 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9099\n", 586 | "Epoch 217/300\n", 587 | "6/6 [==============================] - 0s 2ms/sample - loss: 2.0130\n", 588 | "Epoch 218/300\n", 589 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.8828\n", 590 | "Epoch 219/300\n", 591 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.8979\n", 592 | "Epoch 220/300\n", 593 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.9597\n", 594 | "Epoch 221/300\n", 595 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.8511\n", 596 | "Epoch 222/300\n", 597 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.8472\n", 598 | "Epoch 223/300\n", 599 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.8873\n", 600 | "Epoch 224/300\n", 601 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.8062\n", 602 | "Epoch 225/300\n", 603 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.7988\n", 604 | "Epoch 226/300\n", 605 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.8388\n", 606 | "Epoch 227/300\n", 607 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.8011\n", 608 | "Epoch 228/300\n", 609 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.7480\n", 610 | "Epoch 229/300\n", 611 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.7653\n", 612 | "Epoch 230/300\n", 613 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.7819\n", 614 | "Epoch 231/300\n", 615 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.7464\n", 616 | "Epoch 232/300\n", 617 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.6940\n", 618 | "Epoch 233/300\n", 619 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.6715\n", 620 | "Epoch 234/300\n", 621 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.6775\n", 622 | "Epoch 235/300\n", 623 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.6936\n", 624 | "Epoch 236/300\n", 625 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.7174\n", 626 | "Epoch 237/300\n", 627 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.7877\n", 628 | "Epoch 238/300\n", 629 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.8879\n", 630 | "Epoch 239/300\n", 631 | "6/6 [==============================] - 0s 4ms/sample - loss: 2.0143\n", 632 | "Epoch 240/300\n", 633 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.8549\n", 634 | "Epoch 241/300\n", 635 | "6/6 [==============================] - 0s 4ms/sample - loss: 1.6097\n", 636 | "Epoch 242/300\n", 637 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.5326\n", 638 | "Epoch 243/300\n", 639 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.6629\n", 640 | "Epoch 244/300\n", 641 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.8545\n", 642 | "Epoch 245/300\n", 643 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.8130\n", 644 | "Epoch 246/300\n", 645 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.5512\n", 646 | "Epoch 247/300\n", 647 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.4507\n", 648 | "Epoch 248/300\n", 649 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.6335\n", 650 | "Epoch 249/300\n", 651 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.8015\n", 652 | "Epoch 250/300\n", 653 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.6713\n", 654 | "Epoch 251/300\n", 655 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.4089\n", 656 | "Epoch 252/300\n", 657 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.4223\n", 658 | "Epoch 253/300\n", 659 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.6435\n", 660 | "Epoch 254/300\n", 661 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.6494\n", 662 | "Epoch 255/300\n", 663 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.3939\n", 664 | "Epoch 256/300\n", 665 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.2835\n", 666 | "Epoch 257/300\n", 667 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.4087\n", 668 | "Epoch 258/300\n", 669 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.5086\n", 670 | "Epoch 259/300\n", 671 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.3871\n", 672 | "Epoch 260/300\n", 673 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.1942\n", 674 | "Epoch 261/300\n", 675 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.1508\n", 676 | "Epoch 262/300\n", 677 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.2414\n", 678 | "Epoch 263/300\n", 679 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.3458\n", 680 | "Epoch 264/300\n", 681 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.3374\n", 682 | "Epoch 265/300\n", 683 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.2214\n", 684 | "Epoch 266/300\n", 685 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.0389\n", 686 | "Epoch 267/300\n", 687 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.9397\n", 688 | "Epoch 268/300\n", 689 | "6/6 [==============================] - 0s 3ms/sample - loss: 0.9868\n", 690 | "Epoch 269/300\n", 691 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.1211\n", 692 | "Epoch 270/300\n", 693 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.3686\n", 694 | "Epoch 271/300\n", 695 | "6/6 [==============================] - 0s 3ms/sample - loss: 1.4540\n", 696 | "Epoch 272/300\n", 697 | "6/6 [==============================] - 0s 2ms/sample - loss: 1.2521\n", 698 | "Epoch 273/300\n", 699 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.8309\n", 700 | "Epoch 274/300\n", 701 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.7313\n", 702 | "Epoch 275/300\n", 703 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.9324\n", 704 | "Epoch 276/300\n", 705 | "6/6 [==============================] - 0s 1ms/sample - loss: 0.9877\n", 706 | "Epoch 277/300\n", 707 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.7614\n", 708 | "Epoch 278/300\n", 709 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.5684\n", 710 | "Epoch 279/300\n", 711 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.6753\n", 712 | "Epoch 280/300\n", 713 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.8323\n", 714 | "Epoch 281/300\n", 715 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.7318\n", 716 | "Epoch 282/300\n", 717 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.5124\n", 718 | "Epoch 283/300\n", 719 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.4190\n", 720 | "Epoch 284/300\n", 721 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.5292\n", 722 | "Epoch 285/300\n", 723 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.6345\n", 724 | "Epoch 286/300\n", 725 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.5117\n", 726 | "Epoch 287/300\n", 727 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.3277\n", 728 | "Epoch 288/300\n", 729 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.3312\n", 730 | "Epoch 289/300\n", 731 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.4326\n", 732 | "Epoch 290/300\n", 733 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.4804\n", 734 | "Epoch 291/300\n", 735 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.3297\n", 736 | "Epoch 292/300\n", 737 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.2082\n", 738 | "Epoch 293/300\n", 739 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.2686\n", 740 | "Epoch 294/300\n", 741 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.3462\n", 742 | "Epoch 295/300\n", 743 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.4136\n", 744 | "Epoch 296/300\n", 745 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.2563\n", 746 | "Epoch 297/300\n", 747 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.1534\n", 748 | "Epoch 298/300\n", 749 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.1822\n", 750 | "Epoch 299/300\n", 751 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.2529\n" 752 | ] 753 | }, 754 | { 755 | "name": "stdout", 756 | "output_type": "stream", 757 | "text": [ 758 | "Epoch 300/300\n", 759 | "6/6 [==============================] - 0s 2ms/sample - loss: 0.2984\n" 760 | ] 761 | }, 762 | { 763 | "data": { 764 | "text/plain": [ 765 | "" 766 | ] 767 | }, 768 | "execution_count": 98, 769 | "metadata": {}, 770 | "output_type": "execute_result" 771 | } 772 | ], 773 | "source": [ 774 | "\n", 775 | "# define model\n", 776 | "model = Sequential()\n", 777 | "model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))\n", 778 | "model.add(LSTM(50, activation='relu'))\n", 779 | "model.add(Dense(1))\n", 780 | "model.compile(optimizer='adam', loss='mse')\n", 781 | "# fit model\n", 782 | "model.fit(X, y, epochs=300, verbose=1)\n" 783 | ] 784 | }, 785 | { 786 | "cell_type": "markdown", 787 | "metadata": {}, 788 | "source": [ 789 | "### Predicting For the next 10 data" 790 | ] 791 | }, 792 | { 793 | "cell_type": "code", 794 | "execution_count": 100, 795 | "metadata": {}, 796 | "outputs": [ 797 | { 798 | "name": "stdout", 799 | "output_type": "stream", 800 | "text": [ 801 | "[222.60294]\n", 802 | "1 day input [196. 210. 222.60293579]\n", 803 | "1 day output [[233.8742]]\n", 804 | "2 day input [210. 222.60293579 233.87420654]\n", 805 | "2 day output [[247.27202]]\n", 806 | "3 day input [222.60294 233.8742 247.27202]\n", 807 | "3 day output [[259.5402]]\n", 808 | "4 day input [233.8742 247.27202 259.5402 ]\n", 809 | "4 day output [[271.7569]]\n", 810 | "5 day input [247.27202 259.5402 271.7569 ]\n", 811 | "5 day output [[284.67233]]\n", 812 | "6 day input [259.5402 271.7569 284.67233]\n", 813 | "6 day output [[297.0426]]\n", 814 | "7 day input [271.7569 284.67233 297.0426 ]\n", 815 | "7 day output [[309.54495]]\n", 816 | "8 day input [284.67233 297.0426 309.54495]\n", 817 | "8 day output [[322.2181]]\n", 818 | "9 day input [297.0426 309.54495 322.2181 ]\n", 819 | "9 day output [[334.66574]]\n", 820 | "[222.60294, 233.8742, 247.27202, 259.5402, 271.7569, 284.67233, 297.0426, 309.54495, 322.2181, 334.66574]\n" 821 | ] 822 | } 823 | ], 824 | "source": [ 825 | "# demonstrate prediction for next 10 days\n", 826 | "x_input = array([187, 196, 210])\n", 827 | "temp_input=list(x_input)\n", 828 | "lst_output=[]\n", 829 | "i=0\n", 830 | "while(i<10):\n", 831 | " \n", 832 | " if(len(temp_input)>3):\n", 833 | " x_input=array(temp_input[1:])\n", 834 | " print(\"{} day input {}\".format(i,x_input))\n", 835 | " #print(x_input)\n", 836 | " x_input = x_input.reshape((1, n_steps, n_features))\n", 837 | " #print(x_input)\n", 838 | " yhat = model.predict(x_input, verbose=0)\n", 839 | " print(\"{} day output {}\".format(i,yhat))\n", 840 | " temp_input.append(yhat[0][0])\n", 841 | " temp_input=temp_input[1:]\n", 842 | " #print(temp_input)\n", 843 | " lst_output.append(yhat[0][0])\n", 844 | " i=i+1\n", 845 | " else:\n", 846 | " x_input = x_input.reshape((1, n_steps, n_features))\n", 847 | " yhat = model.predict(x_input, verbose=0)\n", 848 | " print(yhat[0])\n", 849 | " temp_input.append(yhat[0][0])\n", 850 | " lst_output.append(yhat[0][0])\n", 851 | " i=i+1\n", 852 | " \n", 853 | "\n", 854 | "print(lst_output)" 855 | ] 856 | }, 857 | { 858 | "cell_type": "code", 859 | "execution_count": null, 860 | "metadata": {}, 861 | "outputs": [], 862 | "source": [] 863 | }, 864 | { 865 | "cell_type": "code", 866 | "execution_count": 101, 867 | "metadata": {}, 868 | "outputs": [ 869 | { 870 | "data": { 871 | "text/plain": [ 872 | "[110, 125, 133, 146, 158, 172, 187, 196, 210]" 873 | ] 874 | }, 875 | "execution_count": 101, 876 | "metadata": {}, 877 | "output_type": "execute_result" 878 | } 879 | ], 880 | "source": [ 881 | "timeseries_data" 882 | ] 883 | }, 884 | { 885 | "cell_type": "code", 886 | "execution_count": 102, 887 | "metadata": {}, 888 | "outputs": [ 889 | { 890 | "data": { 891 | "text/plain": [ 892 | "9" 893 | ] 894 | }, 895 | "execution_count": 102, 896 | "metadata": {}, 897 | "output_type": "execute_result" 898 | } 899 | ], 900 | "source": [ 901 | "len(timeseries_data)" 902 | ] 903 | }, 904 | { 905 | "cell_type": "code", 906 | "execution_count": 84, 907 | "metadata": {}, 908 | "outputs": [ 909 | { 910 | "data": { 911 | "text/plain": [ 912 | "[207.60309,\n", 913 | " 219.40913,\n", 914 | " 232.47244,\n", 915 | " 249.33023,\n", 916 | " 263.42752,\n", 917 | " 279.1716,\n", 918 | " 296.92273,\n", 919 | " 313.7915,\n", 920 | " 332.32684,\n", 921 | " 352.21432]" 922 | ] 923 | }, 924 | "execution_count": 84, 925 | "metadata": {}, 926 | "output_type": "execute_result" 927 | } 928 | ], 929 | "source": [ 930 | "lst_output" 931 | ] 932 | }, 933 | { 934 | "cell_type": "code", 935 | "execution_count": 75, 936 | "metadata": {}, 937 | "outputs": [ 938 | { 939 | "data": { 940 | "text/plain": [ 941 | "[170, 180, 190]" 942 | ] 943 | }, 944 | "execution_count": 75, 945 | "metadata": {}, 946 | "output_type": "execute_result" 947 | } 948 | ], 949 | "source": [ 950 | "lst" 951 | ] 952 | }, 953 | { 954 | "cell_type": "markdown", 955 | "metadata": {}, 956 | "source": [ 957 | "### Visualizaing The Output" 958 | ] 959 | }, 960 | { 961 | "cell_type": "code", 962 | "execution_count": 38, 963 | "metadata": {}, 964 | "outputs": [], 965 | "source": [ 966 | "import matplotlib.pyplot as plt" 967 | ] 968 | }, 969 | { 970 | "cell_type": "code", 971 | "execution_count": 89, 972 | "metadata": {}, 973 | "outputs": [], 974 | "source": [ 975 | "day_new=np.arange(1,10)\n", 976 | "day_pred=np.arange(10,20)" 977 | ] 978 | }, 979 | { 980 | "cell_type": "code", 981 | "execution_count": 103, 982 | "metadata": {}, 983 | "outputs": [ 984 | { 985 | "data": { 986 | "text/plain": [ 987 | "[]" 988 | ] 989 | }, 990 | "execution_count": 103, 991 | "metadata": {}, 992 | "output_type": "execute_result" 993 | }, 994 | { 995 | "data": { 996 | "image/png": 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\n", 997 | "text/plain": [ 998 | "
" 999 | ] 1000 | }, 1001 | "metadata": { 1002 | "needs_background": "light" 1003 | }, 1004 | "output_type": "display_data" 1005 | } 1006 | ], 1007 | "source": [ 1008 | "plt.plot(day_new,timeseries_data)\n", 1009 | "plt.plot(day_pred,lst_output)\n" 1010 | ] 1011 | }, 1012 | { 1013 | "cell_type": "code", 1014 | "execution_count": null, 1015 | "metadata": {}, 1016 | "outputs": [], 1017 | "source": [] 1018 | } 1019 | ], 1020 | "metadata": { 1021 | "kernelspec": { 1022 | "display_name": "Python 3", 1023 | "language": "python", 1024 | "name": "python3" 1025 | }, 1026 | "language_info": { 1027 | "codemirror_mode": { 1028 | "name": "ipython", 1029 | "version": 3 1030 | }, 1031 | "file_extension": ".py", 1032 | "mimetype": "text/x-python", 1033 | "name": "python", 1034 | "nbconvert_exporter": "python", 1035 | "pygments_lexer": "ipython3", 1036 | "version": "3.7.4" 1037 | } 1038 | }, 1039 | "nbformat": 4, 1040 | "nbformat_minor": 2 1041 | } 1042 | --------------------------------------------------------------------------------