();
332 | for (int i = 0; i < len; ++i) {
333 | map.add(new Pair(i, data[i]));
334 | }
335 | map.sort(new PairValueComparator());
336 | int[] returnOrder = new int[len];
337 | for (int i = 0; i < len; ++i) {
338 | returnOrder[i] = map.get(i).key;
339 | }
340 | return returnOrder;
341 | }
342 | }
343 |
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/src/main/java/com/github/ruananswer/statistics/OnlineNormalStatistics.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.statistics;
2 |
3 | /**
4 | * Simple, fast, online normal statistics object using Welford's algorithm.
5 | */
6 | public class OnlineNormalStatistics {
7 |
8 | private int _n = 0;
9 | private double _mean = 0;
10 | private double _sumSqDiff = 0;
11 |
12 | public OnlineNormalStatistics() {
13 | // do nothing
14 | }
15 |
16 | public OnlineNormalStatistics(double[] initialValues) {
17 | for (double d : initialValues) {
18 | addValue(d);
19 | }
20 | }
21 |
22 | public void addValue(double value) {
23 | if (Double.isNaN(value))
24 | return;
25 | double old_mean = _mean;
26 | _n++;
27 | _mean += (value - old_mean) / _n;
28 | _sumSqDiff += (value - _mean) * (value - old_mean);
29 | }
30 |
31 | public int getN() {
32 | return _n;
33 | }
34 |
35 | public double getMean() {
36 | return (_n > 0) ? _mean : Double.NaN;
37 | }
38 |
39 | public double getSumSqDev() {
40 | return (_n > 0) ? _sumSqDiff : Double.NaN;
41 | }
42 |
43 | public double getVariance() {
44 | return (_n > 1) ? _sumSqDiff / (_n - 1) : Double.NaN;
45 | }
46 |
47 | public double getPopulationVariance() {
48 | return (_n > 0) ? _sumSqDiff / _n : Double.NaN;
49 | }
50 |
51 | public double getStandardScore(double value) {
52 | return (value - _mean) / Math.sqrt(getVariance());
53 | }
54 |
55 | public void set_n(int _n) {
56 | this._n = _n;
57 | }
58 |
59 | public void set_mean(double _mean) {
60 | this._mean = _mean;
61 | }
62 |
63 | public void set_sumSqDiff(double _sumSqDiff) {
64 | this._sumSqDiff = _sumSqDiff;
65 | }
66 |
67 | public double getSumOfSq() {
68 | return _sumSqDiff + _n * _mean * _mean;
69 | }
70 | }
71 |
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/src/main/java/com/github/ruananswer/statistics/QuickMedians.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.statistics;
2 |
3 | import java.util.Arrays;
4 |
5 | /**
6 | * Created by on 17-2-28.
7 | */
8 | public class QuickMedians {
9 | private double[] values;
10 | private double _median = 0;
11 | private int _n = 0;
12 |
13 | public QuickMedians(double[] initialValues) {
14 | _n = initialValues.length;
15 | values = Arrays.copyOf(initialValues, _n);
16 | if (_n > 0) {
17 | if (_n % 2 == 0)
18 | _median = (quickSelectK(_n / 2 - 1) + quickSelectK(_n / 2)) / 2.0;
19 | else
20 | _median = quickSelectK(_n / 2);
21 | }
22 | else
23 | _median = Double.NaN;
24 | }
25 |
26 | public int partition(int low, int high) {
27 | int l = low, r = high, i = low - 1;
28 | double x = values[high];
29 | for (int j = l; j < r; ++j) {
30 | if (values[j] <= x) {
31 | ++i;
32 | swap(i, j);
33 | }
34 | }
35 | swap(i + 1, r);
36 | return i + 1;
37 | }
38 |
39 | private void swap(int i, int j) {
40 | double tmp = values[i];
41 | values[i] = values[j];
42 | values[j] = tmp;
43 | }
44 |
45 | public double quickSelectK(int k) {
46 | int l = 0, r = _n - 1, idx = 0, len = 0;
47 | while (l < r) {
48 | idx = partition(l, r);
49 | len = idx - l + 1;
50 | if (len == k + 1)
51 | return values[idx];
52 | else if (len < k + 1) {
53 | k = k - len;
54 | l = idx + 1;
55 | }
56 | else
57 | r = idx - 1;
58 | }
59 | return values[l];
60 | }
61 |
62 | public double getMedian() { return +_median; }
63 | }
64 |
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/src/main/java/com/github/ruananswer/stl/STLDecomposition.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.stl;
2 |
3 | import java.util.ArrayList;
4 |
5 | /**
6 | * Implementation of STL: A Seasonal-Trend Decomposition Procedure based on Loess.
7 | *
8 | * Robert B. Cleveland et al., "STL: A Seasonal-Trend Decomposition Procedure based on Loess,"
9 | * in Journal of Official Statistics Vol. 6 No. 1, 1990, pp. 3-73
10 | *
11 | *
12 | * Hafen, R. P. "Local regression models: Advancements, applications, and new methods." (2010).
13 | *
14 | * Created by on 16-4-14.
15 | */
16 | public class STLDecomposition {
17 | private final Config config;
18 |
19 | public STLDecomposition(Config config) {
20 | config.check();
21 | this.config = config;
22 | }
23 |
24 | public static class Config {
25 | /** The number of observations in each cycle of the seasonal component, n_p */
26 | private int numObsPerPeriod = -1;
27 | /** s.window either the character string \code{"periodic"} or the span (in lags) of the loess window for seasonal extraction,
28 | * which should be odd. This has no default. */
29 | private int sWindow = -1;
30 | /** s.degree degree of locally-fitted polynomial in seasonal extraction. Should be 0, 1, or 2. */
31 | private int sDegree = 1;
32 | /** t.window the span (in lags) of the loess window for trend extraction, which should be odd.
33 | * If \code{NULL}, the default, \code{nextodd(ceiling((1.5*period) / (1-(1.5/s.window))))}, is taken.*/
34 | private int tWindow = -1;
35 | /** t.degree degree of locally-fitted polynomial in trend extraction. Should be 0, 1, or 2. */
36 | private int tDegree = 1;
37 | /** l.window the span (in lags) of the loess window of the low-pass filter used for each subseries.
38 | * Defaults to the smallest odd integer greater than or equal to \code{n.p}
39 | * which is recommended since it prevents competition between the trend and seasonal components.
40 | * If not an odd integer its given value is increased to the next odd one.*/
41 | private int lWindow = -1;
42 | /** l.degree degree of locally-fitted polynomial for the subseries low-pass filter. Should be 0, 1, or 2. */
43 | private int lDegree = 1;
44 | /** s.jump s.jump,t.jump,l.jump,fc.jump integers at least one to increase speed of the respective smoother.
45 | * Linear interpolation happens between every \code{*.jump}th value. */
46 | private int sJump = -1;
47 | /** t.jump */
48 | private int tJump = -1;
49 | /** l.jump */
50 | private int lJump = -1;
51 | /** critfreq the critical frequency to use for automatic calculation of smoothing windows for the trend and high-pass filter. */
52 | private double critFreq = 0.05;
53 | /** The number of passes through the inner loop, n_i */
54 | private int numberOfInnerLoopPasses = 2;
55 | /** The number of robustness iterations of the outer loop, n_o */
56 | private int numberOfRobustnessIterations = 1;
57 | /** sub.labels optional vector of length n.p that contains the labels of the subseries in their natural order (such as month name, day of week, etc.),
58 | * used for strip labels when plotting. All entries must be unique. */
59 | private int[] subLabels = null;
60 | /** the number of series to decompose */
61 | private int numberOfDataPoints = -1;
62 |
63 | public int getNumObsPerPeriod() {
64 | return numObsPerPeriod;
65 | }
66 |
67 | public void setNumObsPerPeriod(int numObsPerPeriod) {
68 | this.numObsPerPeriod = numObsPerPeriod;
69 | }
70 |
71 | public int getsWindow() {
72 | return sWindow;
73 | }
74 |
75 | public void setsWindow(int sWindow) {
76 | this.sWindow = sWindow;
77 | }
78 |
79 | public int getsDegree() {
80 | return sDegree;
81 | }
82 |
83 | public void setsDegree(int sDegree) {
84 | this.sDegree = sDegree;
85 | }
86 |
87 | public int gettWindow() {
88 | return tWindow;
89 | }
90 |
91 | public void settWindow(int tWindow) {
92 | this.tWindow = tWindow;
93 | }
94 |
95 | public int gettDegree() {
96 | return tDegree;
97 | }
98 |
99 | public void settDegree(int tDegree) {
100 | this.tDegree = tDegree;
101 | }
102 |
103 | public int getlWindow() {
104 | return lWindow;
105 | }
106 |
107 | public void setlWindow(int lWindow) {
108 | this.lWindow = lWindow;
109 | }
110 |
111 | public int getlDegree() {
112 | return lDegree;
113 | }
114 |
115 | public void setlDegree(int lDegree) {
116 | this.lDegree = lDegree;
117 | }
118 |
119 | public int getsJump() {
120 | return sJump;
121 | }
122 |
123 | public void setsJump(int sJump) {
124 | this.sJump = sJump;
125 | }
126 |
127 | public int gettJump() {
128 | return tJump;
129 | }
130 |
131 | public void settJump(int tJump) {
132 | this.tJump = tJump;
133 | }
134 |
135 | public int getlJump() {
136 | return lJump;
137 | }
138 |
139 | public void setlJump(int lJump) {
140 | this.lJump = lJump;
141 | }
142 |
143 | public double getCritFreq() {
144 | return critFreq;
145 | }
146 |
147 | public void setCritFreq(double critFreq) {
148 | this.critFreq = critFreq;
149 | }
150 |
151 | public int getNumberOfInnerLoopPasses() {
152 | return numberOfInnerLoopPasses;
153 | }
154 |
155 | public void setNumberOfInnerLoopPasses(int numberOfInnerLoopPasses) {
156 | this.numberOfInnerLoopPasses = numberOfInnerLoopPasses;
157 | }
158 |
159 | public int getNumberOfRobustnessIterations() {
160 | return numberOfRobustnessIterations;
161 | }
162 |
163 | public void setNumberOfRobustnessIterations(int numberOfRobustnessIterations) {
164 | this.numberOfRobustnessIterations = numberOfRobustnessIterations;
165 | }
166 |
167 | public int[] getSubLabels() {
168 | return subLabels;
169 | }
170 |
171 | public void setSubLabels(int[] subLabels) {
172 | this.subLabels = subLabels;
173 | }
174 |
175 | public int getNumberOfDataPoints() {
176 | return numberOfDataPoints;
177 | }
178 |
179 | public void setNumberOfDataPoints(int numberOfDataPoints) {
180 | this.numberOfDataPoints = numberOfDataPoints;
181 | }
182 |
183 | public Config() {
184 | }
185 |
186 | public void check() {
187 | checkPeriodicity(numObsPerPeriod, numberOfDataPoints);
188 | }
189 |
190 | private boolean checkPeriodicity(int numObsPerPeriod, int numberOfDataPoints) {
191 | if (numObsPerPeriod == -1)
192 | throw new IllegalArgumentException("Must specify periodicity of seasonal");
193 | if (numObsPerPeriod < 4) {
194 | throw new IllegalArgumentException("Periodicity (numObsPerPeriod) must be >= 4");
195 | }
196 | if (numberOfDataPoints <= 2 * numObsPerPeriod) {
197 | throw new IllegalArgumentException(
198 | "numberOfDataPoints(total length) must contain at least 2 * Periodicity (numObsPerPeriod) points");
199 | }
200 | return true;
201 | }
202 | }
203 |
204 | /**
205 | * Decompose a time series into seasonal, trend and irregular components using \code{loess}, acronym STL.
206 | * A new implementation of STL. Allows for NA values, local quadratic smoothing, post-trend smoothing, and endpoint blending.
207 | * The usage is very similar to that of R's built-in \code{stl()}.
208 | * */
209 | public STLResult decompose(long[] times, double[] series) {
210 | if (times == null || series == null || times.length != series.length)
211 | throw new IllegalArgumentException("times must be same length as time series");
212 | int n = series.length;
213 | int numObsPerPeriod = config.getNumObsPerPeriod();
214 | double[] trend = new double[n];
215 | double[] seasonal = new double[n];
216 | double[] remainder = new double[n];
217 |
218 | for (int i = 0; i < n; i++) {
219 | trend[i] = 0.0;
220 | seasonal[i] = 0.0;
221 | remainder[i] = 0.0;
222 | }
223 |
224 | if (config.getlWindow() == -1)
225 | config.setlWindow(STLUtility.nextOdd(config.getNumObsPerPeriod()));
226 | else
227 | config.setlWindow(STLUtility.nextOdd(config.getlWindow()));
228 |
229 |
230 | if (config.getSubLabels() == null) {
231 | int[] idx = new int[n];
232 | for (int i = 0; i < n; ++i)
233 | idx[i] = i % numObsPerPeriod + 1 ;
234 | config.setSubLabels(idx);
235 | }
236 |
237 | config.setsWindow(10 * n + 1);
238 | config.setsDegree(0);
239 | config.setsJump((int)Math.ceil(config.getsWindow() / 10.0));
240 |
241 | if (config.gettWindow() == -1) {
242 | /** Or use t.window <- nextodd(ceiling(1.5 * n.p/(1 - 1.5 / s.window))) */
243 | config.settWindow(STLUtility.getTWindow(config.gettDegree(), config.getsDegree(), config.getsWindow(), numObsPerPeriod, config.getCritFreq()));
244 | }
245 |
246 | if (config.getsJump() == -1) config.setsJump((int)Math.ceil((double)config.getsWindow() / 10.0));
247 | if (config.gettJump() == -1) config.settJump((int)Math.ceil((double)config.gettWindow() / 10.0));
248 | if (config.getlJump() == -1) config.setlJump((int)Math.ceil((double)config.getlWindow() / 10.0));
249 |
250 | /** start and end indices for after adding in extra n.p before and after */
251 | int startIdx = numObsPerPeriod, endIdx = n - 1 + numObsPerPeriod;
252 |
253 | /** cycleSubIndices will keep track of what part of the
254 | # seasonal each observation belongs to */
255 | int[] cycleSubIndices = new int[n];
256 | double[] weight = new double[n];
257 | for (int i = 0; i < n; ++i) {
258 | cycleSubIndices[i] = i % numObsPerPeriod + 1;
259 | weight[i] = 1.0;
260 | }
261 | // subLabels !!
262 | int lenC = n + 2 * numObsPerPeriod;
263 | double[] C = new double[lenC];
264 | double[] D = new double[n];
265 | double[] detrend = new double[n];
266 | int tempSize = (int)Math.ceil((double)n / (double)numObsPerPeriod) / 2;
267 | ArrayList cycleSub = new ArrayList(tempSize), subWeights = new ArrayList(tempSize);
268 | int[] cs1 = new int[numObsPerPeriod], cs2 = new int[numObsPerPeriod];
269 | for (int i = 0; i < numObsPerPeriod; ++i) {
270 | cs1[i] = cycleSubIndices[i];
271 | cs2[i] = cycleSubIndices[n - numObsPerPeriod + i];
272 | }
273 |
274 | double[] ma3, L = new double[n];
275 | int ljump = config.getlJump(), tjump = config.gettJump();
276 | int lenLev = (int)Math.ceil((double)n / (double)ljump), lenTev = (int)Math.ceil((double)n / (double)tjump);
277 | int[] lEv = new int[lenLev], tEv = new int[lenTev];
278 | double weightMeanAns = 0.0;
279 |
280 | for (int oIter = 1; oIter <= config.getNumberOfRobustnessIterations(); ++oIter) {
281 | for (int iIter = 1; iIter <= config.getNumberOfInnerLoopPasses(); ++iIter) {
282 | /** Step 1: detrending */
283 | for (int i = 0; i < n; ++i)
284 | detrend[i] = series[i] - trend[i];
285 |
286 | /** Step 2: smoothing of cycle-subseries */
287 | for (int i = 0; i < numObsPerPeriod; ++i) {
288 | cycleSub.clear(); subWeights.clear();
289 | for (int j = i; j < n; j += numObsPerPeriod) {
290 | if (cycleSubIndices[j] == i + 1) {
291 | cycleSub.add(detrend[j]);
292 | subWeights.add(weight[j]);
293 | }
294 | }
295 | /**
296 | C[c(cs1, cycleSubIndices, cs2) == i] <- rep(weighted.mean(cycleSub,
297 | w = w[cycleSubIndices == i], na.rm = TRUE), cycleSub.length + 2)
298 | */
299 | weightMeanAns = weightMean(cycleSub, subWeights);
300 | for (int j = i; j < numObsPerPeriod; j += numObsPerPeriod)
301 | if (cs1[j] == i + 1)
302 | C[j] = weightMeanAns;
303 | for (int j = i; j < n; j += numObsPerPeriod)
304 | if (cycleSubIndices[j] == i + 1)
305 | C[j + numObsPerPeriod] = weightMeanAns;
306 | for (int j = 0; j < numObsPerPeriod; ++j)
307 | if (cs2[j] == i + 1)
308 | C[j + numObsPerPeriod + n] = weightMeanAns;
309 | }
310 |
311 | /** Step 3: Low-pass filtering of collection of all the cycle-subseries
312 | # moving averages*/
313 | ma3 = STLUtility.cMa(C, numObsPerPeriod);
314 |
315 | for (int i = 0, j = 0; i < lenLev; ++i, j += ljump)
316 | lEv[i] = j + 1;
317 | if (lEv[lenLev - 1] != n) {
318 | int[] tempLev = new int[lenLev + 1];
319 | System.arraycopy(lEv, 0, tempLev, 0, lenLev);
320 | tempLev[lenLev] = n;
321 | L = STLUtility.loessSTL(null, ma3, config.getlWindow(), config.getlDegree(), tempLev, weight, config.getlJump());
322 | } else {
323 | L = STLUtility.loessSTL(null, ma3, config.getlWindow(), config.getlDegree(), lEv, weight, config.getlJump());
324 | }
325 |
326 | /** Step 4: Detrend smoothed cycle-subseries */
327 | /** Step 5: Deseasonalize */
328 | for (int i = 0; i < n; ++i) {
329 | seasonal[i] = C[startIdx + i] - L[i];
330 | D[i] = series[i] - seasonal[i];
331 | }
332 |
333 | /** Step 6: Trend Smoothing */
334 | for (int i = 0, j = 0; i < lenTev; ++i, j += tjump)
335 | tEv[i] = j + 1;
336 | if (tEv[lenTev - 1] != n) {
337 | int[] tempTev = new int[lenTev + 1];
338 | System.arraycopy(tEv, 0, tempTev, 0, lenTev);
339 | tempTev[lenTev] = n;
340 | trend = STLUtility.loessSTL(null, D, config.gettWindow(), config.gettDegree(), tempTev, weight, config.gettJump());
341 | } else {
342 | trend = STLUtility.loessSTL(null, D, config.gettWindow(), config.gettDegree(), tEv, weight, config.gettJump());
343 | }
344 | }
345 |
346 | }
347 | // Calculate remainder
348 | for (int i = 0; i < n; i++) {
349 | remainder[i] = series[i] - trend[i] - seasonal[i];
350 | }
351 | return new STLResult(trend, seasonal, remainder);
352 | }
353 |
354 | private double weightMean(ArrayList x, ArrayList w) {
355 | double sum = 0.0, sumW = 0.0;
356 | int len = x.size();
357 | for (int i = 0; i < len; ++i) {
358 | if (!Double.isNaN(x.get(i))) {
359 | sum += (x.get(i) * w.get(i));
360 | sumW += w.get(i);
361 | }
362 | }
363 | return sum / sumW;
364 | }
365 | }
366 |
--------------------------------------------------------------------------------
/src/main/java/com/github/ruananswer/stl/STLResult.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.stl;
2 |
3 | /**
4 | * The STL decomposition of a time series.
5 | *
6 | * getData() == getTrend() + getSeasonal() + getRemainder()
7 | *
8 | */
9 | public class STLResult {
10 | private final double[] trend;
11 | private final double[] seasonal;
12 | private final double[] remainder;
13 |
14 | public STLResult(double[] trend, double[] seasonal,
15 | double[] remainder) {
16 | this.trend = trend;
17 | this.seasonal = seasonal;
18 | this.remainder = remainder;
19 | }
20 |
21 | public double[] getTrend() {
22 | return trend;
23 | }
24 |
25 | public double[] getSeasonal() {
26 | return seasonal;
27 | }
28 |
29 | public double[] getRemainder() {
30 | return remainder;
31 | }
32 | }
--------------------------------------------------------------------------------
/src/main/java/com/github/ruananswer/stl/STLUtility.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.stl;
2 |
3 | /**
4 | * Created by on 16-4-14.
5 | */
6 | public class STLUtility {
7 | private STLUtility() {}
8 |
9 | public static int nextOdd(double x) {
10 | int xx = (int) Math.round(x);
11 | return (xx%2 == 0) ? (xx + 1) : xx;
12 | }
13 |
14 | public static boolean degreeCheck(int x) {
15 | return (x == 0 || x == 1 || x == 2);
16 | }
17 |
18 | public static int getTWindow(int tDg, int sDg, int nS, int nP, double omega) {
19 | if (tDg == 0) tDg = 1;
20 | if (sDg == 0) sDg = 1;
21 |
22 | double[][] coefsA = {{0.000103350651767650, 3.81086166990428e-6}, {-0.000216653946625270, 0.000708495976681902}};
23 | double[][] coefsB = {{1.42686036792937, 2.24089552678906}, {-3.1503819836694, -3.30435316073732}, {5.07481807116087, 5.08099438760489}};
24 | double[][] coefsC = {{1.66534145060448, 2.33114333880815}, {-3.87719398039131, -1.8314816166323}, {6.46952900183769, 1.85431548427732}};
25 | // estimate critical frequency for seasonal
26 | double betac0 = coefsA[0][0] + coefsA[1][0] * omega;
27 | double betac1 = coefsB[0][0] + coefsB[1][0] * omega + coefsB[2][0] * omega * omega;
28 | double betac2 = coefsC[0][0] + coefsC[1][0] * omega + coefsC[2][0] * omega * omega;
29 |
30 | double fC = (1.0 - (betac0 + betac1 / nS + betac2 / (nS * nS))) / nP;
31 |
32 | double betat0 = coefsA[0][0] + coefsA[1][0] * omega;
33 | double betat1 = coefsB[0][0] + coefsB[1][0] * omega + coefsB[2][0] * omega * omega;
34 | double betat2 = coefsC[0][0] + coefsC[1][0] * omega + coefsC[2][0] * omega * omega;
35 |
36 | double betat00 = betat0 - fC;
37 |
38 | return nextOdd((-betat1 - Math.sqrt(betat1 * betat1 - 4.0 * betat00 * betat2)) / (2.0 * betat00));
39 | }
40 |
41 | public static double[] cMa(double[] x, int nP) {
42 | int i, n = x.length;
43 | int nn = n - nP * 2;
44 | int nn_p = nP;
45 | double ma_tmp = 0.0;
46 |
47 | double[] ans = new double[n - 2 * nn_p];
48 | double[] ma = new double[nn + nn_p + 1];
49 | double[] ma2 = new double[nn + 2];
50 | double[] ma3 = new double[nn];
51 |
52 | ma_tmp = 0;
53 | for(i = 0; i < nn_p; ++i) {
54 | ma_tmp = ma_tmp + x[i];
55 | }
56 | ma[0] = ma_tmp / nn_p;
57 | for(i = nn_p; i < nn + 2 * nn_p; ++i) {
58 | ma_tmp = ma_tmp - x[i - nn_p] + x[i];
59 | ma[i - nn_p + 1] = ma_tmp / nn_p;
60 | }
61 |
62 | ma_tmp = 0;
63 | for(i = 0; i < nn_p; ++i) {
64 | ma_tmp = ma_tmp + ma[i];
65 | }
66 | ma2[0] = ma_tmp / nn_p;
67 |
68 | for(i = nn_p; i < nn + nn_p + 1; ++i) {
69 | ma_tmp = ma_tmp - ma[i - nn_p] + ma[i];
70 | ma2[i - nn_p + 1] = ma_tmp / nn_p;
71 | }
72 |
73 | ma_tmp = 0;
74 |
75 | for(i = 0; i < 3; ++i) {
76 | ma_tmp = ma_tmp + ma2[i];
77 | }
78 | ans[0] = ma_tmp / 3;
79 |
80 | for(i = 3; i < nn + 2; ++i) {
81 | ma_tmp = ma_tmp - ma2[i - 3] + ma2[i];
82 | ans[i - 2] = ma_tmp / 3;
83 | }
84 | return ans;
85 | }
86 |
87 | public static double[] loessSTL(int[] x, double[] y, int span, int degree, int[] m, double[] weights, int jump) {
88 | int n = y.length;
89 | if (x == null) {
90 | x = new int[n];
91 | for (int i = 0; i < n; ++i)
92 | x[i] = i + 1;
93 | }
94 | if (weights == null) {
95 | weights = new double[n];
96 | for (int i = 0; i < n; ++i)
97 | weights[i] = 1.0;
98 | }
99 | int lenM = m.length;
100 |
101 | if (span % 2 == 0)
102 | span += 1;
103 | int s2 = (span + 1) / 2;
104 | int[] lIdx = new int[lenM];
105 | int[] rIdx = new int[lenM];
106 | if (n < span) {
107 | for (int i = 0; i < lenM; ++i) {
108 | lIdx[i] = 1;
109 | rIdx[i] = n;
110 | }
111 | } else {
112 | int countSmallThanS2 = 0, countLargeThanS2 = 0, countLargeThanNMinusS2 = 0;
113 | for (int i = 0; i < lenM; ++i) {
114 | if (m[i] < s2)
115 | ++countSmallThanS2;
116 | else if (m[i] >= s2 && m[i] <= n - s2)
117 | ++countLargeThanS2;
118 | else
119 | ++countLargeThanNMinusS2;
120 | }
121 | int i = 0;
122 | for (; i < countSmallThanS2; ++i)
123 | lIdx[i] = 0;
124 | for (; i < countSmallThanS2 + countLargeThanS2; ++i)
125 | lIdx[i] = m[i] - s2;
126 | for (; i < countSmallThanS2 + countLargeThanS2 + countLargeThanNMinusS2; ++i)
127 | lIdx[i] = n - span;
128 | for (i = 0; i < lenM; ++i)
129 | rIdx[i] = lIdx[i] + span - 1;
130 | }
131 | /**
132 | * aa <- abs(m - x[l_idx])
133 | bb <- abs(x[r_idx] - m)
134 | max_dist <- ifelse(aa > bb, aa, bb)
135 | */
136 | int[] maxDist = new int[lenM];
137 | int aa = 0, bb = 0;
138 | for (int i = 0; i < lenM; ++i) {
139 | aa = Math.abs(m[i] - x[lIdx[i]]);
140 | bb = Math.abs(x[rIdx[i]] - m[i]);
141 | maxDist[i] = (aa > bb) ? aa : bb;
142 | }
143 |
144 | if (span > n) {
145 | for (int i = 0; i < maxDist.length; ++i) {
146 | maxDist[i] = maxDist[i] + (span - n) / 2;
147 | }
148 | }
149 | twoArray out = cLoess(x, y, degree, span, weights, m, lIdx, maxDist);
150 |
151 | // do interpolation
152 | double[] res = new double[out.result.length];
153 | System.arraycopy(out.result, 0, res, 0, out.result.length);
154 | double[] at = new double[n];
155 | for (int i = 0; i < n; ++i)
156 | at[i] = i + 1;
157 | if (jump > 1) {
158 | res = cInterp(m, out.result, out.slopes, at);
159 | }
160 | return res;
161 | }
162 |
163 | public static class twoArray {
164 | public double[] result, slopes;
165 | public twoArray(double[] re, double[] sl) {
166 | result = re;
167 | slopes = sl;
168 | }
169 | }
170 |
171 | private static twoArray cLoess(int[] xx, double[] yy, int degree, int span, double[] ww, int[] m, int[] l_idx, int[] max_dist) {
172 | int span2, span3, offset;
173 | int i, j;
174 | double r, tmp1, tmp2;
175 |
176 | int n = xx.length;
177 | int n_m = m.length;
178 |
179 | double[] x = new double[span];
180 | double[] w = new double[span];
181 | double[] xw = new double[span];
182 | double[] x2w = new double[span];
183 | double[] x3w = new double[span];
184 |
185 | double[] result = new double[n_m];
186 | double[] slopes = new double[n_m];
187 |
188 | // variables for storing determinant intermediate values
189 | double a, b, c, d, e, a1, b1, c1, a2, b2, c2, det;
190 |
191 | span3 = span;
192 | if(span > n) {
193 | span = n;
194 | }
195 |
196 | span2 = (span - 1) / 2;
197 |
198 | // want to start storing results at index 0, corresponding to the lowest m
199 | offset = m[0];
200 |
201 | // loop through all values of m
202 | for(i = 0; i < n_m; i++) {
203 | a = 0.0;
204 |
205 | // get weights, x, and a
206 | for(j = 0; j < span; j++) {
207 | w[j] = 0.0;
208 | x[j] = xx[l_idx[i] + j] - (double)m[i];
209 |
210 | // r = std::fabs(x[j]);
211 | r = (x[j] > 0) ? x[j] : -x[j];
212 | // tricube
213 | tmp1 = r / max_dist[i];
214 | // manual multiplication is much faster than pow()
215 | tmp2 = 1.0 - tmp1 * tmp1 * tmp1;
216 | w[j] = tmp2 * tmp2 * tmp2;
217 |
218 | // scale by user-defined weights
219 | w[j] = w[j] * ww[l_idx[i] + j];
220 |
221 | a = a + w[j];
222 | }
223 |
224 | if(degree == 0) {
225 | // TODO: make sure denominator is not 0
226 | a1 = 1 / a;
227 | for(j = 0; j < span; j++) {
228 | // l_i[j] = w[j] * a1;
229 | result[i] = result[i] + w[j] * a1 * yy[l_idx[i] + j];
230 | }
231 | } else {
232 | // get xw, x2w, b, c for degree 1 or 2
233 | b = 0.0;
234 | c = 0.0;
235 | for(j = 0; j < span; j++) {
236 | xw[j] = x[j] * w[j];
237 | x2w[j] = x[j] * xw[j];
238 | b = b + xw[j];
239 | c = c + x2w[j];
240 | }
241 | if(degree == 1) {
242 | // TODO: make sure denominator is not 0
243 | det = 1 / (a * c - b * b);
244 | a1 = c * det;
245 | b1 = -b * det;
246 | c1 = a * det;
247 | for(j=0; j < span; j++) {
248 | result[i] = result[i] + (w[j] * a1 + xw[j] * b1) * yy[l_idx[i] + j];
249 | slopes[i] = slopes[i] + (w[j] * b1 + xw[j] * c1) * yy[l_idx[i] + j];
250 | }
251 | } else {
252 | // TODO: make sure degree > 2 cannot be specified (and < 0 for that matter)
253 | // get x3w, d, and e for degree 2
254 | d = 0.0;
255 | e = 0.0;
256 | for(j = 0; j < span; j++) {
257 | x3w[j] = x[j] * x2w[j];
258 | d = d + x3w[j];
259 | e = e + x3w[j] * x[j];
260 | }
261 | a1 = e * c - d * d;
262 | b1 = c * d - e * b;
263 | c1 = b * d - c * c;
264 | a2 = c * d - e * b;
265 | b2 = e * a - c * c;
266 | c2 = b * c - d * a;
267 | // TODO: make sure denominator is not 0
268 | det = 1 / (a * a1 + b * b1 + c * c1);
269 | a1 = a1 * det;
270 | b1 = b1 * det;
271 | c1 = c1 * det;
272 | a2 = a2 * det;
273 | b2 = b2 * det;
274 | c2 = c2 * det;
275 | for(j=0; j < span; j++) {
276 | result[i] = result[i] + (w[j] * a1 + xw[j] * b1 + x2w[j] * c1) * yy[l_idx[i] + j];
277 | slopes[i] = slopes[i] + (w[j] * a2 + xw[j] * b2 + x2w[j] * c2) * yy[l_idx[i] + j];
278 | }
279 | }
280 | }
281 | }
282 | return new twoArray(result, slopes);
283 | }
284 |
285 | private static double[] cInterp(int[] m, double[] fits, double[] slopes, double[] at) {
286 | int i, j;
287 | double u, h, u2, u3;
288 |
289 | int n_at = at.length;
290 | double[] ans = new double[n_at];
291 |
292 | j = 0; // index of leftmost vertex
293 | for(i = 0; i < n_at; ++i) {
294 | if(at[i] > m[j + 1]) j++;
295 | h = (m[j + 1] - m[j]);
296 | u = (at[i] - m[j]) / h;
297 | u2 = u * u;
298 | u3 = u2 * u;
299 | ans[i] = (2 * u3 - 3 * u2 + 1) * fits[j] +
300 | (3 * u2 - 2 * u3) * fits[j + 1] +
301 | (u3 - 2 * u2 + u) * slopes[j] * h +
302 | (u3 - u2) * slopes[j + 1] * h;
303 | }
304 | return ans;
305 | }
306 |
307 | }
308 |
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/anomaly/AnomalyDetectionTest.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.anomaly;
2 |
3 | import com.github.ruananswer.testUtility.TestCommon;
4 | import org.testng.Assert;
5 | import org.testng.annotations.BeforeClass;
6 | import org.testng.annotations.DataProvider;
7 | import org.testng.annotations.Test;
8 |
9 | import java.io.IOException;
10 | import java.text.ParseException;
11 | import java.util.ArrayList;
12 | import java.util.Collections;
13 | import java.util.List;
14 |
15 | /**
16 | * Created by ruan on 16-4-7.
17 | */
18 | public class AnomalyDetectionTest {
19 | private ArrayList fileStlNames = new ArrayList();
20 | private ArrayList fileAnomsNames = new ArrayList();
21 | private static final double EqualPercent = 0.95;
22 |
23 | @BeforeClass
24 | public void readTestSets() {
25 | String[] files = TestCommon.readTestSets(" anoms.csv");
26 | for (String a : files)
27 | fileAnomsNames.add(a);
28 | files = TestCommon.readTestSets(" stl.csv");
29 | for (String a : files)
30 | fileStlNames.add(a);
31 | }
32 |
33 | @DataProvider
34 | public Object[][] DateProviderFromFile() throws IOException, ParseException {
35 | List data = new ArrayList();
36 | Collections.sort(fileAnomsNames);
37 | Collections.sort(fileStlNames);
38 | if (fileAnomsNames == null || fileStlNames == null) return null;
39 | Assert.assertEquals(fileAnomsNames.size(), fileStlNames.size());
40 | String fileStl = "", fileAnoms = "";
41 | for (int fileIndex = 0; fileIndex < fileAnomsNames.size(); ++fileIndex) {
42 | fileStl = fileStlNames.get(fileIndex);
43 | fileAnoms = fileAnomsNames.get(fileIndex);
44 | String[] lines = TestCommon.getResourceAsString(fileAnoms).split("\n");
45 | int numAnoms = lines.length;
46 | int[] anomsIndex = new int[numAnoms - 1];
47 | double[] anomsScore = new double[numAnoms - 1];
48 | double maxAnoms = 0.015, alpha = 0.05;
49 | String[] values = lines[0].split(",");
50 | if (values.length > 3) {
51 | maxAnoms = Double.parseDouble(values[3]);
52 | alpha = Double.parseDouble(values[4]);
53 | }
54 | for (int i = 1; i < numAnoms; ++i) {
55 | values = lines[i].split(",");
56 | anomsIndex[i - 1] = Integer.parseInt(values[1]);
57 | anomsScore[i - 1] = Double.parseDouble(values[2]);
58 | }
59 |
60 | lines = TestCommon.getResourceAsString(fileStl).split("\n");
61 | int numData = lines.length;
62 | int seasonality = Integer.parseInt(lines[0].split(",")[0]);
63 | long[] timestamps = new long[numData - 1];
64 | double[] series = new double[numData - 1];
65 | for (int i = 1; i < numData; ++i) {
66 | values = lines[i].split(",");
67 | if (values[0].contains("-"))
68 | timestamps[i - 1] = (int) new java.text.SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(values[0]).getTime();
69 | else
70 | timestamps[i - 1] = Integer.parseInt(values[0]);
71 | String value = values[1];
72 | if (value.equals("NaN")) {
73 | series[i - 1] = Double.NaN;
74 | } else {
75 | series[i - 1] = Double.valueOf(value);
76 | }
77 | }
78 | data.add(new Object[]{fileAnoms, seasonality, maxAnoms, alpha, timestamps, series, anomsIndex, anomsScore});
79 | }
80 | return data.toArray(new Object[][]{});
81 | }
82 |
83 | @Test(dataProvider = "DateProviderFromFile")
84 | public void testFunctionAnomalyDetection(String filename, int seasonality, double maxAnoms, double alpha, long[] timestamps,
85 | double[] series, int[] anomsIndex, double[] anomsScore) throws IOException {
86 | System.out.print(filename);
87 |
88 | DetectAnoms.Config config = new DetectAnoms.Config();
89 | config.setMaxAnoms(maxAnoms);
90 | config.setNumObsPerPeriod(seasonality);
91 | config.setAnomsThreshold(1.0);
92 | config.setAlpha(alpha);
93 | DetectAnoms detectAnoms = new DetectAnoms(config);
94 |
95 | long lStartTime = System.nanoTime();
96 | DetectAnoms.ANOMSResult result = detectAnoms.anomalyDetection(timestamps, series);
97 | long lEndTime = System.nanoTime();
98 | long difference = lEndTime - lStartTime; // check different
99 | System.out.println(" time: " + difference);
100 |
101 | testAnomsSimularity(result, anomsIndex, anomsScore);
102 | }
103 |
104 | private void testAnomsSimularity(DetectAnoms.ANOMSResult res, int[] anomsIndex_r, double[] score_r) {
105 | boolean res_test = (res == null && anomsIndex_r.length != 0);
106 | Assert.assertEquals(res_test, false);
107 | if (res == null && anomsIndex_r.length == 0)
108 | return;
109 | long[] resIndex = res.getAnomsIndex();
110 | int equalCount = 0;
111 | for (int i = 0, j = 0; i < resIndex.length && j < anomsIndex_r.length;) {
112 | if (resIndex[i] + 1 == anomsIndex_r[j]) {
113 | ++equalCount;
114 | ++i;
115 | ++j;
116 | }
117 | else if (resIndex[i] + 1 > anomsIndex_r[j])
118 | ++j;
119 | else
120 | ++i;
121 | }
122 | if (resIndex.length > 0) {
123 | boolean equal_test = (double) equalCount / (double) Math.min(anomsIndex_r.length, resIndex.length) > this.EqualPercent;
124 | Assert.assertEquals(equal_test, true);
125 | }
126 | }
127 | }
128 |
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/src/test/java/com/github/ruananswer/statistics/QuickMedianTest.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.statistics;
2 |
3 | import org.testng.Assert;
4 | import org.testng.annotations.Test;
5 |
6 | import java.util.Arrays;
7 |
8 | /**
9 | * Created by ruan on 16-4-22.
10 | */
11 | public class QuickMedianTest {
12 | @Test
13 | public void simpleArray() {
14 | double[] testArray = {
15 | 2, 1, 6, 5, 4, 3
16 | };
17 | testCorrectness(testArray);
18 | }
19 |
20 | @Test
21 | public void bigArray() {
22 | double[] testArray = new double[1000];
23 | for (int i = 0; i < testArray.length; i++) {
24 | testArray[i] = Math.random();
25 | }
26 | testCorrectness(testArray);
27 | }
28 |
29 | private void testCorrectness(double[] arr) {
30 | QuickMedians stat = new QuickMedians(arr);
31 |
32 | Arrays.sort(arr);
33 | double median = 0;
34 | if (arr.length % 2 == 0)
35 | median = (arr[arr.length / 2 - 1] + arr[arr.length / 2]) / 2.0;
36 | else
37 | median = arr[arr.length / 2];
38 | Assert.assertEquals(stat.getMedian(), median, 0.001);
39 | }
40 | }
41 |
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/stl/PlotTest.java:
--------------------------------------------------------------------------------
1 | /**
2 | * Licensed under the Apache License, Version 2.0 (the "License");
3 | * you may not use this file except in compliance with the License.
4 | * You may obtain a copy of the License at
5 | *
6 | * http://www.apache.org/licenses/LICENSE-2.0
7 | *
8 | * Unless required by applicable law or agreed to in writing, software
9 | * distributed under the License is distributed on an "AS IS" BASIS,
10 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 | * See the License for the specific language governing permissions and
12 | * limitations under the License.
13 | */
14 | package com.github.ruananswer.stl;
15 |
16 | import com.fasterxml.jackson.databind.JsonNode;
17 | import com.fasterxml.jackson.databind.ObjectMapper;
18 | import org.jfree.data.time.Hour;
19 | import org.testng.Assert;
20 | import org.testng.annotations.Test;
21 |
22 | import java.io.File;
23 |
24 | public class PlotTest {
25 |
26 | @Test
27 | public void testPlot() throws Exception {
28 | final ObjectMapper objectMapper = new ObjectMapper();
29 | final JsonNode tree = objectMapper.readTree(new File(this.getClass().getResource("/sample-timeseries.json").getFile()));
30 | final int n = tree.get("times").size();
31 | final long[] ts = new long[n];
32 | final double[] dts = new double[n];
33 | final double[] ys = new double[n];
34 |
35 | for (int i = 0; i < n; i++) {
36 | ts[i] = tree.get("times").get(i).asLong();
37 | dts[i] = ts[i];
38 | ys[i] = tree.get("series").get(i).asDouble();
39 | }
40 |
41 | final STLDecomposition.Config config = new STLDecomposition.Config();
42 | config.setNumberOfDataPoints(n);
43 | config.setNumObsPerPeriod(12);
44 |
45 | final STLDecomposition stl = new STLDecomposition(config);
46 | final STLResult res = stl.decompose(ts, ys);
47 |
48 | final File output = new File("seasonal.png");
49 | final File hourly = new File("stl-hourly.png");
50 |
51 | output.deleteOnExit();
52 | hourly.deleteOnExit();
53 |
54 | StlPlotter.plot(res, ys, dts, "New Title", Hour.class, hourly);
55 | StlPlotter.plot(res, ys, dts, output);
56 | StlPlotter.plot(res, ys, dts);
57 |
58 | Assert.assertTrue(output.exists());
59 | Assert.assertTrue(hourly.exists());
60 |
61 | final File exists = new File("stl-decomposition.png");
62 | exists.deleteOnExit();
63 |
64 | StlPlotter.plot(res, ys, dts, "Test Title");
65 |
66 | Assert.assertTrue(exists.exists());
67 | }
68 | }
69 |
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/stl/STLTest.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.stl;
2 |
3 | import com.github.ruananswer.testUtility.TestCommon;
4 | import org.apache.commons.math3.stat.correlation.PearsonsCorrelation;
5 | import org.apache.commons.math3.stat.inference.TTest;
6 | import org.testng.Assert;
7 | import org.testng.annotations.BeforeClass;
8 | import org.testng.annotations.DataProvider;
9 | import org.testng.annotations.Test;
10 |
11 | import java.io.IOException;
12 | import java.util.ArrayList;
13 | import java.util.List;
14 |
15 | /**
16 | * Created by ruan on 16-4-6.
17 | */
18 |
19 | public class STLTest {
20 | private ArrayList filenames = new ArrayList();
21 | private static final double SimularityPearsonThreshold = 0.8;
22 |
23 | @BeforeClass
24 | public void readTestSets() {
25 | String[] files = TestCommon.readTestSets(" stl.csv");
26 | for (String a : files)
27 | filenames.add(a);
28 | }
29 |
30 | @DataProvider
31 | public Object[][] DateProviderFromFile() throws IOException {
32 | List data = new ArrayList();
33 | for (String filename : filenames) {
34 | String[] lines = TestCommon.getResourceAsString(filename).split("\n");
35 | int numData = lines.length;
36 | int seasonality = Integer.parseInt(lines[0].split(",")[0]);
37 | long[] timestamps = new long[numData - 1];
38 | double[] series = new double[numData - 1];
39 | double[] trend_r = new double[numData - 1];
40 | double[] residual_r = new double[numData - 1];
41 | double[] seasonal_r = new double[numData - 1];
42 | for (int i = 1; i < numData; ++i) {
43 | String[] values = lines[i].split(",");
44 | timestamps[i - 1] = Integer.parseInt(values[0]);
45 | String value = values[1];
46 | if (value.equals("NA")) {
47 | series[i - 1] = Double.NaN;
48 | } else {
49 | series[i - 1] = Double.valueOf(value);
50 | }
51 | trend_r[i - 1] = Double.valueOf(values[2]);
52 | seasonal_r[i - 1] = Double.valueOf(values[3]);
53 | residual_r[i - 1] = Double.valueOf(values[4]);
54 | }
55 | TestCommon.removeMissingValuesByAveragingNeighbors(series);
56 | numData -= 1;
57 | data.add(new Object[] {filename, numData, seasonality, timestamps, series, trend_r, residual_r, seasonal_r});
58 | }
59 | return data.toArray(new Object[][]{});
60 | }
61 |
62 | @Test
63 | public void stlTestNumber() {
64 | Assert.assertEquals(filenames.size(), 22);
65 | }
66 |
67 | @Test(dataProvider = "DateProviderFromFile")
68 | public void testFunctionSTL(String filename, int numData, int seasonality, long[] timestamps, double[] series, double[] trend_r, double[] residual_r, double[] seasonal_r)
69 | throws IOException {
70 | System.out.print(filename);
71 |
72 | STLDecomposition.Config config = new STLDecomposition.Config();
73 | config.setNumberOfDataPoints(numData);
74 | config.setNumObsPerPeriod(seasonality);
75 | // if robust
76 | config.setNumberOfInnerLoopPasses(1);
77 | config.setNumberOfRobustnessIterations(15);
78 |
79 | STLDecomposition stl = new STLDecomposition(config);
80 |
81 | long lStartTime = System.nanoTime();
82 | STLResult res = stl.decompose(timestamps, series);
83 | long lEndTime = System.nanoTime();
84 | long difference = lEndTime - lStartTime; // check different
85 | System.out.println(" time: " + difference);
86 |
87 | calSimularity(res, seasonality, trend_r, residual_r, seasonal_r);
88 | }
89 |
90 | private void calSimularity(STLResult res, int seasonality, double[] trend_r, double[] residual_r, double[] seasonal_r) throws IOException {
91 | double[] data_trend = res.getTrend();
92 | double[] data_remainder = res.getRemainder();
93 | double[] data_seasonal = res.getSeasonal();
94 | double[] data_seasonal_check = new double[seasonality];
95 | double[] seasonal_r_check = new double[seasonality];
96 |
97 | double trend_pearson = CheckTimeSeriesSimularityPearson(trend_r, data_trend);
98 | double remainder_pearson = CheckTimeSeriesSimularityPearson(residual_r, data_remainder);
99 | double seasonal_pearson = CheckTimeSeriesSimularityPearson(seasonal_r, data_seasonal);
100 | boolean trend_pearson_test = trend_pearson > SimularityPearsonThreshold;
101 | boolean remainder_pearson_test = remainder_pearson > SimularityPearsonThreshold;
102 | boolean seasonal_pearson_test = seasonal_pearson > SimularityPearsonThreshold;
103 | System.out.println(trend_pearson + "," + remainder_pearson + "," + seasonal_pearson);
104 | Assert.assertEquals(trend_pearson_test, true);
105 | Assert.assertEquals(remainder_pearson_test, true);
106 | Assert.assertEquals(seasonal_pearson_test, true);
107 | boolean reject_null = checkTimeSeriesSimularityTtest(seasonal_r_check, data_seasonal_check);
108 | Assert.assertEquals(reject_null, false);
109 | }
110 |
111 | private boolean checkTimeSeriesSimularityTtest(double[] r_generated, double[] java_generated) {
112 | TTest t_test_obj = new TTest();
113 | // Performs a paired t-test evaluating the null hypothesis that the mean of the paired
114 | // differences between sample1
115 | // and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not
116 | // equal to 0,
117 | // with significance level 0.05.
118 | double p_value = t_test_obj.tTest(r_generated, java_generated);
119 | boolean reject_null_hyphothesis = (p_value < 0.05);
120 | return reject_null_hyphothesis;
121 | }
122 |
123 | private double CheckTimeSeriesSimularityPearson(double[] r_generated, double[] java_generated) {
124 | PearsonsCorrelation pearson = new PearsonsCorrelation();
125 | double pearson_correlation = pearson.correlation(r_generated, java_generated);
126 | return pearson_correlation;
127 |
128 | }
129 |
130 | }
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/stl/STLUtilityTest.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.stl;
2 |
3 | import com.github.ruananswer.testUtility.TestCommon;
4 | import org.junit.Test;
5 | import org.testng.Assert;
6 |
7 | import java.io.IOException;
8 |
9 | /**
10 | * Created by ruan on 16-4-14.
11 | */
12 | public class STLUtilityTest {
13 | @Test
14 | public void TestLoess() throws IOException{
15 | // test loessSTL
16 | int n = 100;
17 | int[] x = new int[n];
18 | double[] y = new double[n];
19 | int[] m = new int[n];
20 | for (int i = 0; i < n; ++i) {
21 | x[i] = i + 1;
22 | m[i] = i + 1;
23 | }
24 | String[] lines = TestCommon.getResourceAsString("testLoess.csv").split("\n");
25 | int numData = lines.length;
26 | for (int i = 1; i < numData; i++) {
27 | String[] values = lines[i].split(",");
28 | String value = values[1];
29 | if (value.equals("NA")) {
30 | y[i - 1] = Double.NaN;
31 | } else {
32 | y[i - 1] = Double.valueOf(value);
33 | }
34 | }
35 | double[] res = STLUtility.loessSTL(x, y, 31, 0, m, null, (int)Math.ceil(31 / 10));
36 | Assert.assertEquals(res.length, y.length);
37 | return;
38 | }
39 |
40 | }
41 |
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/stl/StlPlotter.java:
--------------------------------------------------------------------------------
1 | /**
2 | * Licensed under the Apache License, Version 2.0 (the "License");
3 | * you may not use this file except in compliance with the License.
4 | * You may obtain a copy of the License at
5 | *
6 | * http://www.apache.org/licenses/LICENSE-2.0
7 | *
8 | * Unless required by applicable law or agreed to in writing, software
9 | * distributed under the License is distributed on an "AS IS" BASIS,
10 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 | * See the License for the specific language governing permissions and
12 | * limitations under the License.
13 | */
14 | package com.github.ruananswer.stl;
15 |
16 | import org.jfree.chart.ChartPanel;
17 | import org.jfree.chart.ChartUtilities;
18 | import org.jfree.chart.JFreeChart;
19 | import org.jfree.chart.axis.DateAxis;
20 | import org.jfree.chart.axis.NumberAxis;
21 | import org.jfree.chart.plot.CombinedDomainXYPlot;
22 | import org.jfree.chart.plot.PlotOrientation;
23 | import org.jfree.chart.plot.XYPlot;
24 | import org.jfree.chart.renderer.xy.ClusteredXYBarRenderer;
25 | import org.jfree.chart.renderer.xy.XYLineAndShapeRenderer;
26 | import org.jfree.data.time.Minute;
27 | import org.jfree.data.time.RegularTimePeriod;
28 | import org.jfree.data.time.TimeSeries;
29 | import org.jfree.data.time.TimeSeriesCollection;
30 | import org.jfree.data.xy.XYDataset;
31 | import org.jfree.ui.ApplicationFrame;
32 | import org.jfree.ui.RefineryUtilities;
33 |
34 | import java.awt.*;
35 | import java.io.BufferedReader;
36 | import java.io.File;
37 | import java.io.IOException;
38 | import java.io.InputStreamReader;
39 | import java.util.ArrayList;
40 | import java.util.Date;
41 | import java.util.List;
42 | import java.util.TimeZone;
43 |
44 | public class StlPlotter {
45 |
46 |
47 | static void plot(final STLResult stlResult, double[] series, double[] times) throws IOException {
48 | StlPlotter.plot(stlResult, series, times, "Seasonal Decomposition");
49 | }
50 |
51 | static void plot(final STLResult stlResult, double[] series, double[] times, final File save) throws IOException {
52 | StlPlotter.plot(stlResult, series, times, "Seasonal Decomposition", Minute.class, save);
53 | }
54 |
55 | static void plot(final STLResult stlResult, double[] series, double[] times, final String title) throws IOException {
56 | StlPlotter.plot(stlResult, series, times, title, Minute.class, new File("stl-decomposition.png"));
57 | }
58 |
59 | static void plot(final STLResult stlResult, double[] series, double[] times, final String title, final Class timePeriod, final File save) throws IOException {
60 | final ResultsPlot plot = new ResultsPlot(stlResult, series, times, title, timePeriod);
61 | ChartUtilities.saveChartAsPNG(save, plot.chart, 800, 600);
62 | }
63 |
64 | static void plotOnScreen(final STLResult stlResult, double[] series, double[] times, final String title) {
65 | final ResultsPlot plot = new ResultsPlot(stlResult, series, times, title, Minute.class);
66 | plot.pack();
67 | RefineryUtilities.centerFrameOnScreen(plot);
68 | plot.setVisible(true);
69 | }
70 |
71 | private static class ResultsPlot extends ApplicationFrame {
72 |
73 | private static final long serialVersionUID = 1L;
74 | private final JFreeChart chart;
75 | private final ChartPanel chartPanel;
76 | private final String title;
77 |
78 | private final Class timePeriod;
79 | private final double[] series;
80 | private final double[] seasonal;
81 | private final double[] trend;
82 | private final double[] times;
83 | private final double[] remainder;
84 |
85 | ResultsPlot(final STLResult stlResults, double[] series, double[] times, final String title, final Class timePeriod) {
86 | super(title);
87 |
88 | this.series = series;
89 | this.seasonal = stlResults.getSeasonal();
90 | this.trend = stlResults.getTrend();
91 | this.times = times;
92 | this.remainder = stlResults.getRemainder();
93 |
94 | this.timePeriod = timePeriod;
95 | this.title = title;
96 |
97 | this.chart = createChart();
98 | this.chart.removeLegend();
99 |
100 | this.chartPanel = new ChartPanel(chart, true, true, true, true, true);
101 | this.chartPanel.setPreferredSize(new Dimension(1000, 500));
102 |
103 | setContentPane(this.chartPanel);
104 | }
105 |
106 | private JFreeChart createChart() {
107 |
108 | final CombinedDomainXYPlot plot = new CombinedDomainXYPlot(new DateAxis("Time"));
109 | final XYLineAndShapeRenderer renderer = new XYLineAndShapeRenderer(true, false);
110 | final ClusteredXYBarRenderer barRenderer = new ClusteredXYBarRenderer();
111 | final GradientPaint black = new GradientPaint(0.0f, 0.0f, Color.black, 0.0f, 0.0f, Color.black);
112 |
113 | final TimeSeries seriests = new TimeSeries("Series");
114 | final TimeSeries seasonalts = new TimeSeries("Seasonal");
115 | final TimeSeries trendts = new TimeSeries("Trend");
116 | final TimeSeries remainderts = new TimeSeries("Remainder");
117 |
118 | final TimeSeries[] tsArray = new TimeSeries[]{seriests, seasonalts, trendts};
119 | final String[] labels = new String[]{"Series", "Seasonal", "Trend"};
120 |
121 | for (int i = 0; i < series.length; i++) {
122 | final Date d = new Date((long) times[i]);
123 | RegularTimePeriod rtp = RegularTimePeriod.createInstance(this.timePeriod, d, TimeZone.getDefault());
124 | seriests.addOrUpdate(rtp, series[i]);
125 | seasonalts.addOrUpdate(rtp, seasonal[i]);
126 | trendts.addOrUpdate(rtp, trend[i]);
127 | remainderts.addOrUpdate(rtp, remainder[i]);
128 | }
129 |
130 | plot.setGap(10.0);
131 | renderer.setSeriesPaint(0, black);
132 | barRenderer.setSeriesPaint(0, black);
133 | plot.setOrientation(PlotOrientation.VERTICAL);
134 |
135 | for (int i = 0; i < tsArray.length; i++) {
136 | final XYDataset ts = new TimeSeriesCollection(tsArray[i]);
137 | final XYPlot p = new XYPlot(ts, new DateAxis(labels[i]), new NumberAxis(labels[i]), renderer);
138 | plot.add(p);
139 | }
140 |
141 | final XYDataset rts = new TimeSeriesCollection(remainderts);
142 | final XYDataset sts = new TimeSeriesCollection(seriests);
143 | final XYDataset tts = new TimeSeriesCollection(trendts);
144 | final XYPlot rplot = new XYPlot(rts, new DateAxis(), new NumberAxis("Remainder"), barRenderer);
145 | final XYPlot seriesAndTrend = new XYPlot(sts, new DateAxis(), new NumberAxis("S & T"), renderer);
146 |
147 | seriesAndTrend.setDataset(1, tts);
148 | seriesAndTrend.setRenderer(1, renderer);
149 |
150 | plot.add(rplot);
151 | plot.add(seriesAndTrend);
152 |
153 | return new JFreeChart(this.title, JFreeChart.DEFAULT_TITLE_FONT, plot, true);
154 | }
155 | }
156 |
157 | public static void main(String[] args) throws Exception {
158 | List times = new ArrayList();
159 | List series = new ArrayList();
160 | List trend = new ArrayList();
161 | List seasonal = new ArrayList();
162 | List remainder = new ArrayList();
163 |
164 | // Read from STDIN
165 | String line;
166 | BufferedReader reader = new BufferedReader(new InputStreamReader(System.in));
167 | while ((line = reader.readLine()) != null) {
168 | String[] tokens = line.split(",");
169 | times.add(Double.valueOf(tokens[0]));
170 | series.add(Double.valueOf(tokens[1]));
171 | trend.add(Double.valueOf(tokens[2]));
172 | seasonal.add(Double.valueOf(tokens[3]));
173 | remainder.add(Double.valueOf(tokens[4]));
174 | }
175 |
176 |
177 | STLResult res = new STLResult(
178 | convert(trend),
179 | convert(seasonal),
180 | convert(remainder));
181 |
182 | double[] tmpSeries = convert(series);
183 | double[] tmpTimes = convert(times);
184 |
185 | if (args.length == 1) {
186 | plot(res, tmpSeries, tmpTimes, new File(args[0]));
187 | } else {
188 | plotOnScreen(res, tmpSeries, tmpTimes, "Seasonal Decomposition");
189 | }
190 | }
191 |
192 | private static double[] convert(List list) {
193 | double[] array = new double[list.size()];
194 | for (int i = 0; i < list.size(); i++) {
195 | array[i] = list.get(i);
196 | }
197 | return array;
198 | }
199 | }
200 |
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/testUtility/StlFilenameFilter.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.testUtility;
2 |
3 | import java.io.File;
4 | import java.io.FilenameFilter;
5 |
6 | /**
7 | * Created by ruan on 16-4-18.
8 | */
9 | public class StlFilenameFilter implements FilenameFilter {
10 | private String type;
11 |
12 | public StlFilenameFilter(String regex) {
13 | this.type = regex;
14 | }
15 | @Override
16 | public boolean accept(File dir, String name) {
17 | return name.endsWith(type);
18 | }
19 | }
20 |
--------------------------------------------------------------------------------
/src/test/java/com/github/ruananswer/testUtility/TestCommon.java:
--------------------------------------------------------------------------------
1 | package com.github.ruananswer.testUtility;
2 |
3 | import org.apache.commons.io.IOUtils;
4 |
5 | import java.io.File;
6 | import java.io.IOException;
7 | import java.io.InputStream;
8 | import java.io.StringWriter;
9 |
10 | /**
11 | * Created by ruan on 16-4-18.
12 | */
13 | public class TestCommon {
14 | public static String getResourceAsString(String resource) throws IOException {
15 | InputStream is = ClassLoader.getSystemResourceAsStream(resource);
16 | StringWriter writer = new StringWriter();
17 | IOUtils.copy(is, writer);
18 | return writer.toString();
19 | }
20 |
21 | public static String[] readTestSets(String regex) {
22 | File f = new File("./src/test/resources/");
23 | StlFilenameFilter filter = (new StlFilenameFilter(regex));
24 | String[] files = f.list(filter);
25 | return files;
26 | }
27 |
28 | public static void removeMissingValuesByAveragingNeighbors(double[] arr) {
29 | for (int i = 0; i < arr.length; i++) {
30 | if (Double.isNaN(arr[i])) {
31 | double sum = 0.0;
32 | int count = 0;
33 | if (i - 1 >= 0 && !Double.isNaN(arr[i - 1])) {
34 | sum += arr[i - 1];
35 | count++;
36 | }
37 | if (i + 1 < arr.length && !Double.isNaN(arr[i + 1])) {
38 | sum += arr[i + 1];
39 | count++;
40 | }
41 | arr[i] = sum / count;
42 | }
43 | }
44 | }
45 | }
46 |
--------------------------------------------------------------------------------
/src/test/resources/bumpInDoublePick anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.49,0.05
2 | 1,2370,0.5,,
3 | 2,2371,0.5,,
4 | 3,2372,0.5,,
5 | 4,2373,0.5,,
6 | 5,2374,0.5,,
7 | 6,2375,0.5,,
8 |
--------------------------------------------------------------------------------
/src/test/resources/bumpToEarly anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,1606,0.8502410142,,
3 | 2,1607,0.8329186071,,
4 | 3,1608,0.8443452923,,
5 | 4,1609,0.8597705242,,
6 | 5,1610,0.8395361853,,
7 | 6,1611,0.8898678571,,
8 | 7,1612,0.8719036736,,
9 | 8,1613,0.8242203038,,
10 | 9,1614,0.8503055231,,
11 | 10,1615,0.8519712322,,
12 |
--------------------------------------------------------------------------------
/src/test/resources/exponentialGrow anoms.csv:
--------------------------------------------------------------------------------
1 | ,,,0.49,0.05
2 |
--------------------------------------------------------------------------------
/src/test/resources/flat anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,535,201.8938007813,,
3 |
--------------------------------------------------------------------------------
/src/test/resources/floor anoms.csv:
--------------------------------------------------------------------------------
1 | ,,,0.015,0.05
2 |
--------------------------------------------------------------------------------
/src/test/resources/gc anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.49,0.05
2 | 1,744,4.5418456866,,
3 | 2,1307,4.5462604406,,
4 | 3,1352,6.4224320144,,
5 | 4,1837,4.7041536052,,
6 | 5,2280,5.4202340241,,
7 | 6,2327,6.5665861158,,
8 | 7,2773,5.0101269728,,
9 | 8,3737,4.7128195562,,
10 | 9,3748,4.8509093238,,
11 | 10,3768,6.8015624596,,
12 | 11,4193,4.7763715336,,
13 | 12,5288,5.0075100129,,
14 | 13,5294,6.019687543,,
15 | 14,5311,4.7815913945,,
16 | 15,5332,5.4960473018,,
17 | 16,5370,5.7774941482,,
18 | 17,5512,11.519791187,,
19 | 18,5513,9.4360954577,,
20 | 19,5631,6.5767649163,,
21 | 20,6448,4.8566719258,,
22 | 21,6492,4.5466641899,,
23 | 22,6588,5.619809918,,
24 | 23,6594,4.7063194164,,
25 | 24,6614,5.4295103948,,
26 | 25,7014,4.619387647,,
27 | 26,7047,5.5676983623,,
28 | 27,7082,5.6331372895,,
29 | 28,7135,5.6413841554,,
30 | 29,8115,4.7759863222,,
31 | 30,8202,5.6284495871,,
32 | 31,8524,4.8570711273,,
33 |
--------------------------------------------------------------------------------
/src/test/resources/growSuddenly anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,1618,1.3500737468,,
3 | 2,1627,1.5938015391,,
4 | 3,1632,1.4764668942,,
5 | 4,1634,1.6775015031,,
6 | 5,1636,1.5417662384,,
7 | 6,1640,1.6811372793,,
8 | 7,1641,1.6702894686,,
9 | 8,1643,1.7885492883,,
10 | 9,1646,1.6076222241,,
11 | 10,1648,1.5839282596,,
12 | 11,1650,1.8204621908,,
13 | 12,1651,1.6043029952,,
14 | 13,1653,1.7144508741,,
15 | 14,1655,2.0968582358,,
16 | 15,1658,1.789847141,,
17 | 16,1659,1.6213897192,,
18 | 17,1660,1.7644703724,,
19 | 18,1664,1.6437483517,,
20 | 19,1666,1.7081919169,,
21 | 20,1671,1.7182059554,,
22 | 21,1676,1.8126922324,,
23 | 22,1682,1.5328886635,,
24 | 23,1695,1.5117800161,,
25 | 24,1696,1.4524502916,,
26 | 25,1698,1.4895907281,,
27 | 26,1711,1.1660509048,,
28 |
--------------------------------------------------------------------------------
/src/test/resources/justGrow anoms.csv:
--------------------------------------------------------------------------------
1 | ,,,0.49,0.05
2 |
--------------------------------------------------------------------------------
/src/test/resources/justGrowWithError anoms.csv:
--------------------------------------------------------------------------------
1 | ,,,0.49,0.05
2 |
--------------------------------------------------------------------------------
/src/test/resources/linearGrow anoms.csv:
--------------------------------------------------------------------------------
1 | ,,,0.49,0.05
2 |
--------------------------------------------------------------------------------
/src/test/resources/linearGrowWithError anoms.csv:
--------------------------------------------------------------------------------
1 | ,,,0.49,0.05
2 |
--------------------------------------------------------------------------------
/src/test/resources/moreNoise anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,1655,1.6251919502,,
3 | 2,1715,-0.1083311403,,
4 | 3,1736,0.5404697481,,
5 |
--------------------------------------------------------------------------------
/src/test/resources/plateau anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,1654,0.3875278833,,
3 | 2,1657,0.4442118062,,
4 | 3,1663,0.4184568984,,
5 | 4,1669,0.4506329114,,
6 |
--------------------------------------------------------------------------------
/src/test/resources/removeNoise anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,535,1.3339626094,,
3 |
--------------------------------------------------------------------------------
/src/test/resources/sample-timeseries.json:
--------------------------------------------------------------------------------
1 | {"series": [40.6, 40.8, 44.4, 46.7, 54.1, 58.5, 57.7, 56.4, 54.3, 50.5, 42.9, 39.8, 44.2, 39.8, 45.1, 47.0, 54.1, 58.7, 66.3, 59.9, 57.0, 54.2, 39.7, 42.8, 37.5, 38.7, 39.5, 42.1, 55.7, 57.8, 56.8, 54.3, 54.3, 47.1, 41.8, 41.7, 41.8, 40.1, 42.9, 45.8, 49.2, 52.7, 64.2, 59.6, 54.4, 49.2, 36.3, 37.6, 39.3, 37.5, 38.3, 45.5, 53.2, 57.7, 60.8, 58.2, 56.4, 49.8, 44.4, 43.6, 40.0, 40.5, 40.8, 45.1, 53.8, 59.4, 63.5, 61.0, 53.0, 50.0, 38.1, 36.3, 39.2, 43.4, 43.4, 48.9, 50.6, 56.8, 62.5, 62.0, 57.5, 46.7, 41.6, 39.8, 39.4, 38.5, 45.3, 47.1, 51.7, 55.0, 60.4, 60.5, 54.7, 50.3, 42.3, 35.2, 40.8, 41.1, 42.8, 47.3, 50.9, 56.4, 62.2, 60.5, 55.4, 50.2, 43.0, 37.3, 34.8, 31.3, 41.0, 43.9, 53.1, 56.9, 62.5, 60.3, 59.8, 49.2, 42.9, 41.9, 41.6, 37.1, 41.2, 46.9, 51.2, 60.4, 60.1, 61.6, 57.0, 50.9, 43.0, 38.8, 37.1, 38.4, 38.4, 46.5, 53.5, 58.4, 60.6, 58.2, 53.8, 46.6, 45.5, 40.6, 42.4, 38.4, 40.3, 44.6, 50.9, 57.0, 62.1, 63.5, 56.3, 47.3, 43.6, 41.8, 36.2, 39.3, 44.5, 48.7, 54.2, 60.8, 65.5, 64.9, 60.1, 50.2, 42.1, 35.8, 39.4, 38.2, 40.4, 46.9, 53.4, 59.6, 66.5, 60.4, 59.2, 51.2, 42.8, 45.8, 40.0, 42.6, 43.5, 47.1, 50.0, 60.5, 64.6, 64.0, 56.8, 48.6, 44.2, 36.4, 37.3, 35.0, 44.0, 43.9, 52.7, 58.6, 60.0, 61.1, 58.1, 49.6, 41.6, 41.3, 40.8, 41.0, 38.4, 47.4, 54.1, 58.6, 61.4, 61.8, 56.3, 50.9, 41.4, 37.1, 42.1, 41.2, 47.3, 46.6, 52.4, 59.0, 59.6, 60.4, 57.0, 50.7, 47.8, 39.2, 39.4, 40.9, 42.4, 47.8, 52.4, 58.0, 60.7, 61.8, 58.2, 46.7, 46.6, 37.8], "times": [-1577894400, -1575216000, -1572710400, -1570032000, -1567440000, -1564761600, -1562169600, -1559491200, -1556812800, -1554220800, -1551542400, -1548950400, -1546272000, -1543593600, -1541174400, -1538496000, -1535904000, -1533225600, -1530633600, -1527955200, -1525276800, -1522684800, -1520006400, -1517414400, -1514736000, -1512057600, -1509638400, -1506960000, -1504368000, -1501689600, -1499097600, -1496419200, -1493740800, -1491148800, -1488470400, -1485878400, -1483200000, -1480521600, -1478102400, -1475424000, -1472832000, -1470153600, -1467561600, -1464883200, -1462204800, -1459612800, -1456934400, -1454342400, -1451664000, -1448985600, -1446480000, -1443801600, -1441209600, -1438531200, -1435939200, -1433260800, -1430582400, -1427990400, -1425312000, -1422720000, -1420041600, -1417363200, -1414944000, -1412265600, -1409673600, -1406995200, -1404403200, -1401724800, -1399046400, -1396454400, -1393776000, -1391184000, -1388505600, -1385827200, -1383408000, -1380729600, -1378137600, -1375459200, -1372867200, -1370188800, -1367510400, -1364918400, -1362240000, -1359648000, -1356969600, -1354291200, -1351872000, -1349193600, -1346601600, -1343923200, -1341331200, -1338652800, -1335974400, -1333382400, -1330704000, -1328112000, -1325433600, -1322755200, -1320249600, -1317571200, -1314979200, -1312300800, -1309708800, -1307030400, -1304352000, -1301760000, -1299081600, -1296489600, -1293811200, -1291132800, -1288713600, -1286035200, -1283443200, -1280764800, -1278172800, -1275494400, -1272816000, -1270224000, -1267545600, -1264953600, -1262275200, -1259596800, -1257177600, -1254499200, -1251907200, -1249228800, -1246636800, -1243958400, -1241280000, -1238688000, -1236009600, -1233417600, -1230739200, -1228060800, -1225641600, -1222963200, -1220371200, -1217692800, -1215100800, -1212422400, -1209744000, -1207152000, -1204473600, -1201881600, -1199203200, -1196524800, -1194019200, -1191340800, -1188748800, -1186070400, -1183478400, -1180800000, -1178121600, -1175529600, -1172851200, -1170259200, -1167580800, -1164902400, -1162483200, -1159804800, -1157212800, -1154534400, -1151942400, -1149264000, -1146585600, -1143993600, -1141315200, -1138723200, -1136044800, -1133366400, -1130947200, -1128268800, -1125676800, -1122998400, -1120406400, -1117728000, -1115049600, -1112457600, -1109779200, -1107187200, -1104508800, -1101830400, -1099411200, -1096732800, -1094140800, -1091462400, -1088870400, -1086192000, -1083513600, -1080921600, -1078243200, -1075651200, -1072972800, -1070294400, -1067788800, -1065110400, -1062518400, -1059840000, -1057248000, -1054569600, -1051891200, -1049299200, -1046620800, -1044028800, -1041350400, -1038672000, -1036252800, -1033574400, -1030982400, -1028304000, -1025712000, -1023033600, -1020355200, -1017763200, -1015084800, -1012492800, -1009814400, -1007136000, -1004716800, -1002038400, -999446400, -996768000, -994176000, -991497600, -988819200, -986227200, -983548800, -980956800, -978278400, -975600000, -973180800, -970502400, -967910400, -965232000, -962640000, -959961600, -957283200, -954691200, -952012800, -949420800]}
2 |
--------------------------------------------------------------------------------
/src/test/resources/smallChange anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,45,0.7707388789,,
3 | 2,203,0.7707388789,,
4 | 3,361,0.7707388789,,
5 | 4,519,0.7707388789,,
6 | 5,677,0.7707388789,,
7 | 6,680,0.8075581004,,
8 | 7,835,0.7707388789,,
9 | 8,838,0.8075581004,,
10 | 9,993,0.7707388789,,
11 | 10,996,0.8075581004,,
12 | 11,1151,0.7707388789,,
13 | 12,1154,0.8075581004,,
14 | 13,1309,0.7707388789,,
15 | 14,1312,0.8075581004,,
16 | 15,1467,0.7707388789,,
17 | 16,1470,0.8075581004,,
18 | 17,1620,0.6776739762,,
19 | 18,1621,0.7039393817,,
20 | 19,1622,0.7742022371,,
21 | 20,1623,0.79512986,,
22 | 21,1624,0.7501411264,,
23 | 22,1625,0.8672997114,,
24 | 23,1626,0.7654968626,,
25 | 24,1627,0.8073618989,,
26 | 25,1628,0.8820028725,,
27 | 26,1629,0.7760696791,,
28 |
--------------------------------------------------------------------------------
/src/test/resources/smallChangeOnStrictModel anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,40,0.7032794192,,
3 | 2,198,0.7032794192,,
4 | 3,356,0.7032794192,,
5 | 4,514,0.7032794192,,
6 | 5,672,0.7032794192,,
7 | 6,830,0.7032794192,,
8 | 7,988,0.7032794192,,
9 | 8,1146,0.7032794192,,
10 | 9,1304,0.7032794192,,
11 | 10,1462,0.7032794192,,
12 | 11,1620,0.7022794192,,
13 |
--------------------------------------------------------------------------------
/src/test/resources/speark anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,535,1.2009617617,,
3 | 2,1186,1.5,,
4 |
--------------------------------------------------------------------------------
/src/test/resources/speark stl.csv:
--------------------------------------------------------------------------------
1 | 158,all,,,
2 | 1,0.9786621309,1.0095868936,0.0076647966,-0.0385895594
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1218 | 1217,1.0920298962,1.0030235577,0.0252004326,0.0638059059
1219 | 1218,1.1518570432,1.0030084793,0.0010731428,0.1477754212
1220 | 1219,0.9654528406,1.0029942691,0.0017973523,-0.0393387808
1221 | 1220,0.9178358656,1.0029797527,-0.031610808,-0.0535330791
1222 | 1221,1.0414452923,1.0029637658,0.0051714989,0.0333100277
1223 | 1222,1.025895437,1.0029464422,0.0166142647,0.0063347301
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1226 | 1225,0.9792577713,1.002887788,0.0080929,-0.0317229167
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1240 | 1239,1.0676681387,1.0025770051,0.030106469,0.0349846646
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1251 | 1250,0.9850479543,1.0022887478,0.022225182,-0.0394659755
1252 | 1251,1.0540715578,1.0022563907,0.0136665505,0.0381486166
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1254 | 1253,0.9301152564,1.0021916783,-0.0066364067,-0.0654400152
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1262 | 1261,0.994130941,1.001966001,0.0206402901,-0.0284753502
1263 | 1262,0.9937104874,1.0019357066,0.0147839197,-0.0230091389
1264 | 1263,0.9418261922,1.0018980926,-0.0290318571,-0.0310400432
1265 | 1264,1.0197337088,1.0018674255,0.0199328156,-0.0020665324
1266 |
--------------------------------------------------------------------------------
/src/test/resources/stopSuddenly anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.015,0.05
2 | 1,1644,0,,
3 | 2,1645,0,,
4 | 3,1646,0,,
5 | 4,1647,0,,
6 | 5,1650,0,,
7 | 6,1651,0,,
8 | 7,1652,0,,
9 | 8,1653,0,,
10 | 9,1654,0,,
11 | 10,1655,0,,
12 | 11,1656,0,,
13 | 12,1657,0,,
14 | 13,1658,0,,
15 | 14,1659,0,,
16 | 15,1660,0,,
17 | 16,1661,0,,
18 | 17,1662,0,,
19 | 18,1663,0,,
20 | 19,1664,0,,
21 | 20,1666,0,,
22 | 21,1667,0,,
23 | 22,1669,0,,
24 | 23,1670,0,,
25 | 24,1672,0,,
26 | 25,1676,0,,
27 | 26,1678,0,,
28 |
--------------------------------------------------------------------------------
/src/test/resources/testLoess.csv:
--------------------------------------------------------------------------------
1 | "","x"
2 | "1",1.14360804083816
3 | "2",0.504238354273159
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102 |
--------------------------------------------------------------------------------
/src/test/resources/thoughput anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.49,0.05
2 | 1,974,9.6990228908,,
3 | 2,7147,7.8603623729,,
4 |
--------------------------------------------------------------------------------
/src/test/resources/timeSeries anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.49,0.05
2 | 1,1288,14.8105699555,,
3 | 2,1290,14.4963629666,,
4 | 3,1291,14.4740497305,,
5 | 4,1292,14.4621398657,,
6 | 5,1293,14.3943024464,,
7 | 6,1294,14.3246399439,,
8 | 7,1295,14.3418401713,,
9 | 8,1296,14.4317226971,,
10 | 9,1297,14.5031777508,,
11 | 10,1298,14.5141809574,,
12 | 11,1300,14.5554315856,,
13 | 12,1301,14.6435120058,,
14 | 13,1302,14.7373932258,,
15 | 14,1303,14.8057091563,,
16 | 15,1304,14.8314351997,,
17 | 16,1305,14.8387877629,,
18 | 17,1306,14.8129605432,,
19 | 18,1307,14.7758579831,,
20 | 19,1308,14.7613243633,,
21 | 20,1309,14.6996668655,,
22 | 21,1310,14.6448118762,,
23 | 22,1311,14.574792329,,
24 | 23,1312,14.4516253756,,
25 | 24,1313,14.3241331569,,
26 | 25,1314,14.236664615,,
27 | 26,1315,14.2301686145,,
28 | 27,1316,14.2430045715,,
29 | 28,1317,14.2224499122,,
30 | 29,1318,14.1805695573,,
31 | 30,1319,14.2104134147,,
32 | 31,1320,14.2940984865,,
33 | 32,1321,14.3856383295,,
34 | 33,1322,14.4482408451,,
35 | 34,1323,14.4700800986,,
36 | 35,1324,14.5198192168,,
37 | 36,1325,14.5951211738,,
38 | 37,1326,14.6838422202,,
39 | 38,1327,14.7760233217,,
40 | 39,1328,14.8418721743,,
41 | 40,1329,14.8637505315,,
42 | 41,1330,14.8630032214,,
43 | 42,1331,14.8550767699,,
44 | 43,1332,14.8812304408,,
45 | 44,1333,14.8529564114,,
46 | 45,1334,14.8271492871,,
47 | 46,1335,14.7514329221,,
48 | 47,1336,14.6105444274,,
49 | 48,1337,14.5408698138,,
50 | 49,1338,14.566865324,,
51 | 50,1339,14.6985418768,,
52 | 51,1340,14.7322627833,,
53 | 52,1341,14.6722869265,,
54 | 53,1342,14.7072593859,,
55 | 54,1343,14.8499934862,,
56 | 55,1344,15.0344623155,,
57 | 56,1345,15.1744589018,,
58 | 57,1346,15.2207137647,,
59 | 58,1347,15.263846184,,
60 | 59,1348,15.3428475628,,
61 | 60,1349,15.4986919333,,
62 | 61,1350,15.6676814498,,
63 | 62,1351,15.7656870219,,
64 | 63,1352,15.7836368061,,
65 | 64,1353,15.7260151184,,
66 | 65,1354,15.6721570813,,
67 | 66,1355,15.6869277377,,
68 | 67,1356,15.7387758071,,
69 | 68,1357,15.73153512,,
70 | 69,1358,15.6515900564,,
71 | 70,1359,15.4671096638,,
72 |
--------------------------------------------------------------------------------
/src/test/resources/tmp anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.001,0.4
2 | 1,1000,9.9481883299,,
3 | 2,2200,12,,
4 |
--------------------------------------------------------------------------------
/src/test/resources/twitter anoms.csv:
--------------------------------------------------------------------------------
1 | ,index,anoms,0.02,0.05
2 | 1,125,21.351,,
3 | 2,5320,193.1036,,
4 | 3,7431,32.5045,,
5 | 4,7432,30.0555,,
6 | 5,7433,31.2614,,
7 | 6,7435,27.386,,
8 | 7,7436,28.9807,,
9 | 8,7437,29.0844,,
10 | 9,7438,26.9185,,
11 | 10,7439,26.4621,,
12 | 11,7441,40.4268,,
13 | 12,7442,28.8811,,
14 | 13,7443,27.1294,,
15 | 14,7444,26.9913,,
16 | 15,7445,26.7741,,
17 | 16,7446,30.6972,,
18 | 17,7447,27.6085,,
19 | 18,7448,25.2841,,
20 | 19,7449,25.2264,,
21 | 20,7450,25.1927,,
22 | 21,7451,27.6501,,
23 | 22,7452,24.8858,,
24 | 23,7453,24.8099,,
25 | 24,7454,24.295,,
26 | 25,7455,24.5221,,
27 | 26,7456,26.7354,,
28 | 27,7457,24.7589,,
29 | 28,7458,25.63,,
30 | 29,7459,24.6949,,
31 | 30,7460,23.6518,,
32 | 31,7461,27.6977,,
33 | 32,7462,25.8687,,
34 | 33,7463,24.6654,,
35 | 34,7464,24.6694,,
36 | 35,7465,24.4351,,
37 | 36,7466,25.7723,,
38 | 37,7467,24.2502,,
39 | 38,7471,32.8182,,
40 | 39,7472,27.4586,,
41 | 40,7473,26.3163,,
42 | 41,7583,165.787,,
43 | 42,7584,167.585,,
44 | 43,7585,170.349,,
45 | 44,7586,180.899,,
46 | 45,7587,170.513,,
47 | 46,7588,174.678,,
48 | 47,7589,164.735,,
49 | 48,7590,178.822,,
50 | 49,7591,198.326,,
51 | 50,7592,203.901,,
52 | 51,7593,200.309,,
53 | 52,7594,178.491,,
54 | 53,7595,167.748,,
55 | 54,7596,183.018,,
56 | 55,7597,176.769,,
57 | 56,7598,186.823,,
58 | 57,7599,183.66,,
59 | 58,7600,179.276,,
60 | 59,7601,197.283,,
61 | 60,7602,191.097,,
62 | 61,7603,194.67,,
63 | 62,7604,177.325,,
64 | 63,7605,173.758,,
65 | 64,7606,200.816,,
66 | 65,7607,186.235,,
67 | 66,7608,185.421,,
68 | 67,7609,178.958,,
69 | 68,7610,171.75,,
70 | 69,7611,203.231,,
71 | 70,7612,181.354,,
72 | 71,7613,186.778,,
73 | 72,7614,175.582,,
74 | 73,7615,176.125,,
75 | 74,7616,181.514,,
76 | 75,7617,175.261,,
77 | 76,7618,164.719,,
78 | 77,7621,170.736,,
79 | 78,10640,188.2908,,
80 | 79,13636,50.5876,,
81 | 80,13640,38.8363,,
82 | 81,13641,41.0145,,
83 | 82,13642,39.5523,,
84 | 83,13643,38.9117,,
85 | 84,13644,37.3052,,
86 | 85,13645,36.1725,,
87 | 86,13646,37.515,,
88 | 87,13647,38.1387,,
89 | 88,13651,43.6522,,
90 | 89,14358,40,,
91 | 90,14368,250,,
92 | 91,14378,40,,
93 |
--------------------------------------------------------------------------------