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* the Apache License, Version 2.0 (the "License"); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
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package org.elasticsearch.search.aggregations.pipeline.movavg.models;
import org.elasticsearch.common.Nullable;
import org.elasticsearch.common.ParseField;
import org.elasticsearch.common.ParseFieldMatcher;
import org.elasticsearch.common.io.stream.StreamInput;
import org.elasticsearch.common.io.stream.StreamOutput;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.search.aggregations.pipeline.movavg.MovAvgParser;
import java.io.IOException;
import java.text.ParseException;
import java.util.*;
/**
* Calculate a doubly exponential weighted moving average
*/
public class HoltLinearModel extends MovAvgModel {
protected static final ParseField NAME_FIELD = new ParseField("holt");
/**
* Controls smoothing of data. Also known as "level" value.
* Alpha = 1 retains no memory of past values
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
* mean of the series).
*/
private final double alpha;
/**
* Controls smoothing of trend.
* Beta = 1 retains no memory of past values
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
* mean of the series).
*/
private final double beta;
public HoltLinearModel(double alpha, double beta) {
this.alpha = alpha;
this.beta = beta;
}
@Override
public boolean canBeMinimized() {
return true;
}
@Override
public MovAvgModel neighboringModel() {
double newValue = Math.random();
switch ((int) (Math.random() * 2)) {
case 0:
return new HoltLinearModel(newValue, this.beta);
case 1:
return new HoltLinearModel(this.alpha, newValue);
default:
assert (false): "Random value fell outside of range [0-1]";
return new HoltLinearModel(newValue, this.beta); // This should never technically happen...
}
}
@Override
public MovAvgModel clone() {
return new HoltLinearModel(this.alpha, this.beta);
}
/**
* Predicts the next `n` values in the series, using the smoothing model to generate new values.
* Unlike the other moving averages, Holt-Linear has forecasting/prediction built into the algorithm.
* Prediction is more than simply adding the next prediction to the window and repeating. Holt-Linear
* will extrapolate into the future by applying the trend information to the smoothed data.
*
* @param values Collection of numerics to movingAvg, usually windowed
* @param numPredictions Number of newly generated predictions to return
* @param <T> Type of numeric
* @return Returns an array of doubles, since most smoothing methods operate on floating points
*/
@Override
protected <T extends Number> double[] doPredict(Collection<T> values, int numPredictions) {
return next(values, numPredictions);
}
@Override
public <T extends Number> double next(Collection<T> values) {
return next(values, 1)[0];
}
/**
* Calculate a Holt-Linear (doubly exponential weighted) moving average
*
* @param values Collection of values to calculate avg for
* @param numForecasts number of forecasts into the future to return
*
* @param <T> Type T extending Number
* @return Returns a Double containing the moving avg for the window
*/
public <T extends Number> double[] next(Collection<T> values, int numForecasts) {
if (values.size() == 0) {
return emptyPredictions(numForecasts);
}
// Smoothed value
double s = 0;
double last_s = 0;
// Trend value
double b = 0;
double last_b = 0;
int counter = 0;
T last;
for (T v : values) {
last = v;
if (counter == 1) {
s = v.doubleValue();
b = v.doubleValue() - last.doubleValue();
} else {
s = alpha * v.doubleValue() + (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[] forecastValues = new double[numForecasts];
for (int i = 0; i < numForecasts; i++) {
forecastValues[i] = s + (i * b);
}
return forecastValues;
}
public static final MovAvgModelStreams.Stream STREAM = new MovAvgModelStreams.Stream() {
@Override
public MovAvgModel readResult(StreamInput in) throws IOException {
return new HoltLinearModel(in.readDouble(), in.readDouble());
}
@Override
public String getName() {
return NAME_FIELD.getPreferredName();
}
};
@Override
public void writeTo(StreamOutput out) throws IOException {
out.writeString(STREAM.getName());
out.writeDouble(alpha);
out.writeDouble(beta);
}
public static class DoubleExpModelParser extends AbstractModelParser {
@Override
public String getName() {
return NAME_FIELD.getPreferredName();
}
@Override
public MovAvgModel parse(@Nullable Map<String, Object> settings, String pipelineName, int windowSize,
ParseFieldMatcher parseFieldMatcher) throws ParseException {
double alpha = parseDoubleParam(settings, "alpha", 0.3);
double beta = parseDoubleParam(settings, "beta", 0.1);
checkUnrecognizedParams(settings);
return new HoltLinearModel(alpha, beta);
}
}
public static class HoltLinearModelBuilder implements MovAvgModelBuilder {
private Double alpha;
private Double beta;
/**
* Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values
* (e.g. a random walk), while alpha = 0 retains infinite memory of past values (e.g.
* the series mean). Useful values are somewhere in between. Defaults to 0.5.
*
* @param alpha A double between 0-1 inclusive, controls data smoothing
*
* @return The builder to continue chaining
*/
public HoltLinearModelBuilder alpha(double alpha) {
this.alpha = alpha;
return this;
}
/**
* Equivalent to <code>alpha</code>, but controls the smoothing of the trend instead of the data
*
* @param beta a double between 0-1 inclusive, controls trend smoothing
*
* @return The builder to continue chaining
*/
public HoltLinearModelBuilder beta(double beta) {
this.beta = beta;
return this;
}
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.field(MovAvgParser.MODEL.getPreferredName(), NAME_FIELD.getPreferredName());
builder.startObject(MovAvgParser.SETTINGS.getPreferredName());
if (alpha != null) {
builder.field("alpha", alpha);
}
if (beta != null) {
builder.field("beta", beta);
}
builder.endObject();
return builder;
}
}
}