@Test public void testSimplePredictionModel() { MovAvgModel model = new SimpleModel(); int windowSize = randomIntBetween(1, 50); int numPredictions = randomIntBetween(1, 50); EvictingQueue<Double> window = EvictingQueue.create(windowSize); for (int i = 0; i < windowSize; i++) { window.offer(randomDouble()); } double actual[] = model.predict(window, numPredictions); double expected[] = new double[numPredictions]; double t = 0; for (double value : window) { t += value; } t /= window.size(); Arrays.fill(expected, t); for (int i = 0; i < numPredictions; i++) { assertThat(Double.compare(expected[i], actual[i]), equalTo(0)); } }
@Test public void testEWMAPredictionModel() { double alpha = randomDouble(); MovAvgModel model = new EwmaModel(alpha); int windowSize = randomIntBetween(1, 50); int numPredictions = randomIntBetween(1, 50); EvictingQueue<Double> window = EvictingQueue.create(windowSize); for (int i = 0; i < windowSize; i++) { window.offer(randomDouble()); } double actual[] = model.predict(window, numPredictions); double expected[] = new double[numPredictions]; double avg = 0; boolean first = true; for (double value : window) { if (first) { avg = value; first = false; } else { avg = (value * alpha) + (avg * (1 - alpha)); } } Arrays.fill(expected, avg); for (int i = 0; i < numPredictions; i++) { assertThat(Double.compare(expected[i], actual[i]), equalTo(0)); } }
@Test public void testLinearPredictionModel() { MovAvgModel model = new LinearModel(); int windowSize = randomIntBetween(1, 50); int numPredictions = randomIntBetween(1, 50); EvictingQueue<Double> window = EvictingQueue.create(windowSize); for (int i = 0; i < windowSize; i++) { window.offer(randomDouble()); } double actual[] = model.predict(window, numPredictions); double expected[] = new double[numPredictions]; double avg = 0; long totalWeight = 1; long current = 1; for (double value : window) { avg += value * current; totalWeight += current; current += 1; } avg = avg / totalWeight; Arrays.fill(expected, avg); for (int i = 0; i < numPredictions; i++) { assertThat(Double.compare(expected[i], actual[i]), equalTo(0)); } }
@Test public void testHoltLinearPredictionModel() { double alpha = randomDouble(); double beta = randomDouble(); MovAvgModel model = new HoltLinearModel(alpha, beta); int windowSize = randomIntBetween(1, 50); int numPredictions = randomIntBetween(1, 50); EvictingQueue<Double> window = EvictingQueue.create(windowSize); for (int i = 0; i < windowSize; i++) { window.offer(randomDouble()); } double actual[] = model.predict(window, numPredictions); double expected[] = new double[numPredictions]; double s = 0; double last_s = 0; // Trend value double b = 0; double last_b = 0; int counter = 0; double last; for (double value : window) { last = value; if (counter == 1) { s = value; b = value - last; } else { s = alpha * value + (1.0d - alpha) * (last_s + last_b); b = beta * (s - last_s) + (1 - beta) * last_b; } counter += 1; last_s = s; last_b = b; } for (int i = 0; i < numPredictions; i++) { expected[i] = s + (i * b); assertThat(Double.compare(expected[i], actual[i]), equalTo(0)); } }
@Test public void testHoltWintersAdditivePredictionModel() { double alpha = randomDouble(); double beta = randomDouble(); double gamma = randomDouble(); int period = randomIntBetween(1, 10); MovAvgModel model = new HoltWintersModel( alpha, beta, gamma, period, HoltWintersModel.SeasonalityType.ADDITIVE, false); int windowSize = randomIntBetween(period * 2, 50); // HW requires at least two periods of data int numPredictions = randomIntBetween(1, 50); EvictingQueue<Double> window = EvictingQueue.create(windowSize); for (int i = 0; i < windowSize; i++) { window.offer(randomDouble()); } double actual[] = model.predict(window, numPredictions); double expected[] = new double[numPredictions]; // Smoothed value double s = 0; double last_s = 0; // Trend value double b = 0; double last_b = 0; // Seasonal value double[] seasonal = new double[windowSize]; int counter = 0; double[] vs = new double[windowSize]; for (double v : window) { vs[counter] = v; counter += 1; } // Initial level value is average of first season // Calculate the slopes between first and second season for each period for (int i = 0; i < period; i++) { s += vs[i]; b += (vs[i + period] - vs[i]) / period; } s /= (double) period; b /= (double) period; last_s = s; // Calculate first seasonal if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) { Arrays.fill(seasonal, 0.0); } else { for (int i = 0; i < period; i++) { seasonal[i] = vs[i] / s; } } for (int i = period; i < vs.length; i++) { s = alpha * (vs[i] - seasonal[i - period]) + (1.0d - alpha) * (last_s + last_b); b = beta * (s - last_s) + (1 - beta) * last_b; seasonal[i] = gamma * (vs[i] - (last_s - last_b)) + (1 - gamma) * seasonal[i - period]; last_s = s; last_b = b; } for (int i = 1; i <= numPredictions; i++) { int idx = window.size() - period + ((i - 1) % period); expected[i - 1] = s + (i * b) + seasonal[idx]; assertThat(Double.compare(expected[i - 1], actual[i - 1]), equalTo(0)); } }