@Test
public void testLinearMovAvgModel() {
MovAvgModel model = new LinearModel();
int numValues = randomIntBetween(1, 100);
int windowSize = randomIntBetween(1, 50);
EvictingQueue<Double> window = EvictingQueue.create(windowSize);
for (int i = 0; i < numValues; i++) {
double randValue = randomDouble();
if (i == 0) {
window.offer(randValue);
continue;
}
double avg = 0;
long totalWeight = 1;
long current = 1;
for (double value : window) {
avg += value * current;
totalWeight += current;
current += 1;
}
double expected = avg / totalWeight;
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
window.offer(randValue);
}
}
@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 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 testSimpleMovAvgModel() {
MovAvgModel model = new SimpleModel();
int numValues = randomIntBetween(1, 100);
int windowSize = randomIntBetween(1, 50);
EvictingQueue<Double> window = EvictingQueue.create(windowSize);
for (int i = 0; i < numValues; i++) {
double randValue = randomDouble();
double expected = 0;
if (i == 0) {
window.offer(randValue);
continue;
}
for (double value : window) {
expected += value;
}
expected /= window.size();
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
window.offer(randValue);
}
}
@Test
public void testEWMAMovAvgModel() {
double alpha = randomDouble();
MovAvgModel model = new EwmaModel(alpha);
int numValues = randomIntBetween(1, 100);
int windowSize = randomIntBetween(1, 50);
EvictingQueue<Double> window = EvictingQueue.create(windowSize);
for (int i = 0; i < numValues; i++) {
double randValue = randomDouble();
if (i == 0) {
window.offer(randValue);
continue;
}
double avg = 0;
boolean first = true;
for (double value : window) {
if (first) {
avg = value;
first = false;
} else {
avg = (value * alpha) + (avg * (1 - alpha));
}
}
double expected = avg;
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
window.offer(randValue);
}
}
@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 testHoltLinearMovAvgModel() {
double alpha = randomDouble();
double beta = randomDouble();
MovAvgModel model = new HoltLinearModel(alpha, beta);
int numValues = randomIntBetween(1, 100);
int windowSize = randomIntBetween(1, 50);
EvictingQueue<Double> window = EvictingQueue.create(windowSize);
for (int i = 0; i < numValues; i++) {
double randValue = randomDouble();
if (i == 0) {
window.offer(randValue);
continue;
}
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;
}
double expected = s + (0 * b);
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
window.offer(randValue);
}
}
@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));
}
}
@Override
public void updateAfterEvent(Sim sim, Event event) {
for (Item item : sim.getMachine()) {
Object[] row = getRow(item, sim);
// Use an evicting queue so that we maintain a fixed number of
// points per item
if (!lastNRows.containsKey(sim)) {
lastNRows.put(sim, new HashMap<Item, Queue<Object[]>>());
}
Map<Item, Queue<Object[]>> lastNRowsForSim = lastNRows.get(sim);
if (!lastNRowsForSim.containsKey(item)) {
lastNRowsForSim.put(item, EvictingQueue.<Object[]>create(MIN_NUM_POINTS_PER_ITEM));
}
lastNRowsForSim.get(item).add(row);
}
}
@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));
}
}
@Test
public void testHoltWintersMultiplicativePadModel() {
double alpha = randomDouble();
double beta = randomDouble();
double gamma = randomDouble();
int period = randomIntBetween(1, 10);
MovAvgModel model =
new HoltWintersModel(
alpha, beta, gamma, period, HoltWintersModel.SeasonalityType.MULTIPLICATIVE, true);
int windowSize = randomIntBetween(period * 2, 50); // HW requires at least two periods of data
EvictingQueue<Double> window = EvictingQueue.create(windowSize);
for (int i = 0; i < windowSize; i++) {
window.offer(randomDouble());
}
// 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 + 0.0000000001;
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;
}
int idx = window.size() - period + (0 % period);
double expected = (s + (1 * b)) * seasonal[idx];
double actual = model.next(window);
assertThat(Double.compare(expected, actual), equalTo(0));
}