Merge pull request #10964 from polyfractal/feature/aggs_movavg_rename
Rename Moving Average models to their "common" names
This commit is contained in:
Zachary Tong
commit
e70a8d4ee9
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@ -21,9 +21,9 @@ A `moving_avg` aggregation looks like this in isolation:
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[source,js]
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--------------------------------------------------
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{
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"movavg": {
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"moving_avg": {
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"buckets_path": "the_sum",
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"model": "double_exp",
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"model": "holt",
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"window": 5,
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"gap_policy": "insert_zero",
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"settings": {
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@ -153,9 +153,9 @@ although typically less than the `simple` model:
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.Linear moving average with window of size 100
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image::images/reducers_movavg/linear_100window.png[]
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==== Single Exponential
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==== EWMA (Exponentially Weighted)
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The `single_exp` model is similar to the `linear` model, except older data-points become exponentially less important,
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The `ewma` model (aka "single-exponential") is similar to the `linear` model, except older data-points become exponentially less important,
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rather than linearly less important. The speed at which the importance decays can be controlled with an `alpha`
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setting. Small values make the weight decay slowly, which provides greater smoothing and takes into account a larger
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portion of the window. Larger valuers make the weight decay quickly, which reduces the impact of older values on the
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@ -169,7 +169,7 @@ The default value of `alpha` is `0.5`, and the setting accepts any float from 0-
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"the_movavg":{
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"moving_avg":{
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"buckets_path": "the_sum",
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"model" : "single_exp",
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"model" : "ewma",
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"settings" : {
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"alpha" : 0.5
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}
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@ -187,13 +187,13 @@ image::images/reducers_movavg/single_0.2alpha.png[]
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.Single Exponential moving average with window of size 10, alpha = 0.7
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image::images/reducers_movavg/single_0.7alpha.png[]
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==== Double Exponential
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==== Holt-Linear
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The `double_exp` model, sometimes called "Holt's Linear Trend" model, incorporates a second exponential term which
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The `holt` model (aka "double exponential") incorporates a second exponential term which
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tracks the data's trend. Single exponential does not perform well when the data has an underlying linear trend. The
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double exponential model calculates two values internally: a "level" and a "trend".
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The level calculation is similar to `single_exp`, and is an exponentially weighted view of the data. The difference is
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The level calculation is similar to `ewma`, and is an exponentially weighted view of the data. The difference is
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that the previously smoothed value is used instead of the raw value, which allows it to stay close to the original series.
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The trend calculation looks at the difference between the current and last value (e.g. the slope, or trend, of the
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smoothed data). The trend value is also exponentially weighted.
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@ -208,7 +208,7 @@ The default value of `alpha` and `beta` is `0.5`, and the settings accept any fl
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"the_movavg":{
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"moving_avg":{
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"buckets_path": "the_sum",
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"model" : "double_exp",
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"model" : "holt",
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"settings" : {
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"alpha" : 0.5,
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"beta" : 0.5
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@ -217,7 +217,7 @@ The default value of `alpha` and `beta` is `0.5`, and the settings accept any fl
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}
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--------------------------------------------------
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In practice, the `alpha` value behaves very similarly in `double_exp` as `single_exp`: small values produce more smoothing
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In practice, the `alpha` value behaves very similarly in `holt` as `ewma`: small values produce more smoothing
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and more lag, while larger values produce closer tracking and less lag. The value of `beta` is often difficult
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to see. Small values emphasize long-term trends (such as a constant linear trend in the whole series), while larger
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values emphasize short-term trends. This will become more apparently when you are predicting values.
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@ -251,14 +251,14 @@ as your buckets:
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}
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--------------------------------------------------
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The `simple`, `linear` and `single_exp` models all produce "flat" predictions: they essentially converge on the mean
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The `simple`, `linear` and `ewma` models all produce "flat" predictions: they essentially converge on the mean
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of the last value in the series, producing a flat:
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[[simple_prediction]]
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.Simple moving average with window of size 10, predict = 50
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image::images/reducers_movavg/simple_prediction.png[]
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In contrast, the `double_exp` model can extrapolate based on local or global constant trends. If we set a high `beta`
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In contrast, the `holt` model can extrapolate based on local or global constant trends. If we set a high `beta`
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value, we can extrapolate based on local constant trends (in this case the predictions head down, because the data at the end
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of the series was heading in a downward direction):
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@ -33,9 +33,9 @@ import java.util.Map;
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/**
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* Calculate a exponentially weighted moving average
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*/
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public class SingleExpModel extends MovAvgModel {
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public class EwmaModel extends MovAvgModel {
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protected static final ParseField NAME_FIELD = new ParseField("single_exp");
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protected static final ParseField NAME_FIELD = new ParseField("ewma");
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/**
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* Controls smoothing of data. Alpha = 1 retains no memory of past values
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@ -44,7 +44,7 @@ public class SingleExpModel extends MovAvgModel {
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*/
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private double alpha;
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public SingleExpModel(double alpha) {
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public EwmaModel(double alpha) {
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this.alpha = alpha;
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}
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@ -68,7 +68,7 @@ public class SingleExpModel extends MovAvgModel {
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public static final MovAvgModelStreams.Stream STREAM = new MovAvgModelStreams.Stream() {
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@Override
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public MovAvgModel readResult(StreamInput in) throws IOException {
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return new SingleExpModel(in.readDouble());
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return new EwmaModel(in.readDouble());
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}
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@Override
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@ -98,11 +98,11 @@ public class SingleExpModel extends MovAvgModel {
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alpha = 0.5;
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}
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return new SingleExpModel(alpha);
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return new EwmaModel(alpha);
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}
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}
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public static class SingleExpModelBuilder implements MovAvgModelBuilder {
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public static class EWMAModelBuilder implements MovAvgModelBuilder {
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private double alpha = 0.5;
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@ -115,7 +115,7 @@ public class SingleExpModel extends MovAvgModel {
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*
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* @return The builder to continue chaining
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*/
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public SingleExpModelBuilder alpha(double alpha) {
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public EWMAModelBuilder alpha(double alpha) {
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this.alpha = alpha;
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return this;
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}
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@ -32,9 +32,9 @@ import java.util.*;
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/**
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* Calculate a doubly exponential weighted moving average
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*/
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public class DoubleExpModel extends MovAvgModel {
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public class HoltLinearModel extends MovAvgModel {
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protected static final ParseField NAME_FIELD = new ParseField("double_exp");
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protected static final ParseField NAME_FIELD = new ParseField("holt");
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/**
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* Controls smoothing of data. Alpha = 1 retains no memory of past values
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@ -48,15 +48,15 @@ public class DoubleExpModel extends MovAvgModel {
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*/
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private double beta;
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public DoubleExpModel(double alpha, double beta) {
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public HoltLinearModel(double alpha, double beta) {
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this.alpha = alpha;
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this.beta = beta;
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}
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/**
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* Predicts the next `n` values in the series, using the smoothing model to generate new values.
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* Unlike the other moving averages, double-exp has forecasting/prediction built into the algorithm.
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* Prediction is more than simply adding the next prediction to the window and repeating. Double-exp
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* Unlike the other moving averages, Holt-Linear has forecasting/prediction built into the algorithm.
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* Prediction is more than simply adding the next prediction to the window and repeating. Holt-Linear
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* will extrapolate into the future by applying the trend information to the smoothed data.
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*
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* @param values Collection of numerics to movingAvg, usually windowed
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@ -75,7 +75,7 @@ public class DoubleExpModel extends MovAvgModel {
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}
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/**
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* Calculate a doubly exponential weighted moving average
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* Calculate a Holt-Linear (doubly exponential weighted) moving average
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*
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* @param values Collection of values to calculate avg for
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* @param numForecasts number of forecasts into the future to return
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@ -99,8 +99,6 @@ public class DoubleExpModel extends MovAvgModel {
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int counter = 0;
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//TODO bail if too few values
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T last;
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for (T v : values) {
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last = v;
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@ -128,7 +126,7 @@ public class DoubleExpModel extends MovAvgModel {
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public static final MovAvgModelStreams.Stream STREAM = new MovAvgModelStreams.Stream() {
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@Override
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public MovAvgModel readResult(StreamInput in) throws IOException {
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return new DoubleExpModel(in.readDouble(), in.readDouble());
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return new HoltLinearModel(in.readDouble(), in.readDouble());
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}
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@Override
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@ -165,11 +163,11 @@ public class DoubleExpModel extends MovAvgModel {
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beta = 0.5;
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}
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return new DoubleExpModel(alpha, beta);
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return new HoltLinearModel(alpha, beta);
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}
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}
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public static class DoubleExpModelBuilder implements MovAvgModelBuilder {
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public static class HoltLinearModelBuilder implements MovAvgModelBuilder {
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private double alpha = 0.5;
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private double beta = 0.5;
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@ -183,7 +181,7 @@ public class DoubleExpModel extends MovAvgModel {
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*
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* @return The builder to continue chaining
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*/
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public DoubleExpModelBuilder alpha(double alpha) {
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public HoltLinearModelBuilder alpha(double alpha) {
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this.alpha = alpha;
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return this;
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}
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@ -195,7 +193,7 @@ public class DoubleExpModel extends MovAvgModel {
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*
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* @return The builder to continue chaining
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*/
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public DoubleExpModelBuilder beta(double beta) {
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public HoltLinearModelBuilder beta(double beta) {
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this.beta = beta;
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return this;
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}
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@ -36,8 +36,8 @@ public class MovAvgModelModule extends AbstractModule {
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public MovAvgModelModule() {
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registerParser(SimpleModel.SimpleModelParser.class);
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registerParser(LinearModel.LinearModelParser.class);
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registerParser(SingleExpModel.SingleExpModelParser.class);
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registerParser(DoubleExpModel.DoubleExpModelParser.class);
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registerParser(EwmaModel.SingleExpModelParser.class);
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registerParser(HoltLinearModel.DoubleExpModelParser.class);
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}
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public void registerParser(Class<? extends MovAvgModelParser> parser) {
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@ -34,8 +34,8 @@ public class TransportMovAvgModelModule extends AbstractModule {
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public TransportMovAvgModelModule() {
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registerStream(SimpleModel.STREAM);
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registerStream(LinearModel.STREAM);
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registerStream(SingleExpModel.STREAM);
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registerStream(DoubleExpModel.STREAM);
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registerStream(EwmaModel.STREAM);
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registerStream(HoltLinearModel.STREAM);
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}
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public void registerStream(MovAvgModelStreams.Stream stream) {
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@ -35,11 +35,11 @@ import org.elasticsearch.search.aggregations.metrics.ValuesSourceMetricsAggregat
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import org.elasticsearch.search.aggregations.reducers.BucketHelpers;
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import org.elasticsearch.search.aggregations.reducers.ReducerHelperTests;
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import org.elasticsearch.search.aggregations.reducers.SimpleValue;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.DoubleExpModel;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.HoltLinearModel;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.LinearModel;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.MovAvgModelBuilder;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.SimpleModel;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.SingleExpModel;
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import org.elasticsearch.search.aggregations.reducers.movavg.models.EwmaModel;
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import org.elasticsearch.test.ElasticsearchIntegrationTest;
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import org.hamcrest.Matchers;
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import org.junit.Test;
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@ -84,7 +84,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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enum MovAvgType {
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SIMPLE ("simple"), LINEAR("linear"), SINGLE("single"), DOUBLE("double");
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SIMPLE ("simple"), LINEAR("linear"), EWMA("ewma"), HOLT("holt");
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private final String name;
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@ -199,11 +199,11 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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case LINEAR:
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values.add(linear(window));
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break;
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case SINGLE:
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values.add(singleExp(window));
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case EWMA:
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values.add(ewma(window));
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break;
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case DOUBLE:
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values.add(doubleExp(window));
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case HOLT:
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values.add(holt(window));
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break;
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}
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@ -246,12 +246,12 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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}
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/**
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* Single exponential moving avg
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* Exponentionally weighted (EWMA, Single exponential) moving avg
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*
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* @param window Window of values to compute movavg for
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* @return
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*/
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private double singleExp(Collection<Double> window) {
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private double ewma(Collection<Double> window) {
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double avg = 0;
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boolean first = true;
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@ -267,11 +267,11 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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}
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/**
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* Double exponential moving avg
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* Holt-Linear (Double exponential) moving avg
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* @param window Window of values to compute movavg for
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* @return
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*/
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private double doubleExp(Collection<Double> window) {
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private double holt(Collection<Double> window) {
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double s = 0;
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double last_s = 0;
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@ -411,7 +411,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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}
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@Test
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public void singleSingleValuedField() {
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public void ewmaSingleValuedField() {
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SearchResponse response = client()
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.prepareSearch("idx").setTypes("type")
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@ -421,12 +421,12 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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.subAggregation(metric)
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.subAggregation(movingAvg("movavg_counts")
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.window(windowSize)
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.modelBuilder(new SingleExpModel.SingleExpModelBuilder().alpha(alpha))
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.modelBuilder(new EwmaModel.EWMAModelBuilder().alpha(alpha))
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.gapPolicy(gapPolicy)
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.setBucketsPaths("_count"))
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.subAggregation(movingAvg("movavg_values")
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.window(windowSize)
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.modelBuilder(new SingleExpModel.SingleExpModelBuilder().alpha(alpha))
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.modelBuilder(new EwmaModel.EWMAModelBuilder().alpha(alpha))
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.gapPolicy(gapPolicy)
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.setBucketsPaths("the_metric"))
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).execute().actionGet();
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@ -439,8 +439,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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List<? extends Bucket> buckets = histo.getBuckets();
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assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
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List<Double> expectedCounts = testValues.get(MovAvgType.SINGLE.toString() + "_" + MetricTarget.COUNT.toString());
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List<Double> expectedValues = testValues.get(MovAvgType.SINGLE.toString() + "_" + MetricTarget.VALUE.toString());
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List<Double> expectedCounts = testValues.get(MovAvgType.EWMA.toString() + "_" + MetricTarget.COUNT.toString());
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List<Double> expectedValues = testValues.get(MovAvgType.EWMA.toString() + "_" + MetricTarget.VALUE.toString());
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Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
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Iterator<ReducerHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
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@ -463,7 +463,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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}
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@Test
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public void doubleSingleValuedField() {
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public void holtSingleValuedField() {
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SearchResponse response = client()
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.prepareSearch("idx").setTypes("type")
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@ -473,12 +473,12 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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.subAggregation(metric)
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.subAggregation(movingAvg("movavg_counts")
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.window(windowSize)
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.modelBuilder(new DoubleExpModel.DoubleExpModelBuilder().alpha(alpha).beta(beta))
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.modelBuilder(new HoltLinearModel.HoltLinearModelBuilder().alpha(alpha).beta(beta))
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.gapPolicy(gapPolicy)
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.setBucketsPaths("_count"))
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.subAggregation(movingAvg("movavg_values")
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.window(windowSize)
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.modelBuilder(new DoubleExpModel.DoubleExpModelBuilder().alpha(alpha).beta(beta))
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.modelBuilder(new HoltLinearModel.HoltLinearModelBuilder().alpha(alpha).beta(beta))
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.gapPolicy(gapPolicy)
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.setBucketsPaths("the_metric"))
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).execute().actionGet();
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@ -491,8 +491,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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List<? extends Bucket> buckets = histo.getBuckets();
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assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
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List<Double> expectedCounts = testValues.get(MovAvgType.DOUBLE.toString() + "_" + MetricTarget.COUNT.toString());
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List<Double> expectedValues = testValues.get(MovAvgType.DOUBLE.toString() + "_" + MetricTarget.VALUE.toString());
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List<Double> expectedCounts = testValues.get(MovAvgType.HOLT.toString() + "_" + MetricTarget.COUNT.toString());
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List<Double> expectedValues = testValues.get(MovAvgType.HOLT.toString() + "_" + MetricTarget.VALUE.toString());
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Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
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Iterator<ReducerHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
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@ -731,7 +731,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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currentValue = current.value();
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if (gapPolicy.equals(BucketHelpers.GapPolicy.SKIP)) {
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// if we are ignoring, movavg could go up (double_exp) or stay the same (simple, linear, single_exp)
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// if we are ignoring, movavg could go up (holt) or stay the same (simple, linear, ewma)
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assertThat(Double.compare(lastValue, currentValue), lessThanOrEqualTo(0));
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} else if (gapPolicy.equals(BucketHelpers.GapPolicy.INSERT_ZEROS)) {
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// If we insert zeros, this should always increase the moving avg since the last bucket has a real value
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@ -790,7 +790,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
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currentValue = current.value();
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if (gapPolicy.equals(BucketHelpers.GapPolicy.SKIP)) {
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// if we are ignoring, movavg could go up (double_exp) or stay the same (simple, linear, single_exp)
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// if we are ignoring, movavg could go up (holt) or stay the same (simple, linear, ewma)
|
||||
assertThat(Double.compare(lastValue, currentValue), lessThanOrEqualTo(0));
|
||||
} else if (gapPolicy.equals(BucketHelpers.GapPolicy.INSERT_ZEROS)) {
|
||||
// If we insert zeros, this should always increase the moving avg since the last bucket has a real value
|
||||
|
|
@ -1056,9 +1056,9 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
case 1:
|
||||
return new LinearModel.LinearModelBuilder();
|
||||
case 2:
|
||||
return new SingleExpModel.SingleExpModelBuilder().alpha(alpha);
|
||||
return new EwmaModel.EWMAModelBuilder().alpha(alpha);
|
||||
case 3:
|
||||
return new DoubleExpModel.DoubleExpModelBuilder().alpha(alpha).beta(beta);
|
||||
return new HoltLinearModel.HoltLinearModelBuilder().alpha(alpha).beta(beta);
|
||||
default:
|
||||
return new SimpleModel.SimpleModelBuilder();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -140,9 +140,9 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
|
|||
}
|
||||
|
||||
@Test
|
||||
public void testSingleExpMovAvgModel() {
|
||||
public void testEWMAMovAvgModel() {
|
||||
double alpha = randomDouble();
|
||||
MovAvgModel model = new SingleExpModel(alpha);
|
||||
MovAvgModel model = new EwmaModel(alpha);
|
||||
|
||||
int numValues = randomIntBetween(1, 100);
|
||||
int windowSize = randomIntBetween(1, 50);
|
||||
|
|
@ -170,9 +170,9 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
|
|||
}
|
||||
|
||||
@Test
|
||||
public void testSinglePredictionModel() {
|
||||
public void testEWMAPredictionModel() {
|
||||
double alpha = randomDouble();
|
||||
MovAvgModel model = new SingleExpModel(alpha);
|
||||
MovAvgModel model = new EwmaModel(alpha);
|
||||
|
||||
int windowSize = randomIntBetween(1, 50);
|
||||
int numPredictions = randomIntBetween(1,50);
|
||||
|
|
@ -206,10 +206,10 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
|
|||
}
|
||||
|
||||
@Test
|
||||
public void testDoubleExpMovAvgModel() {
|
||||
public void testHoltLinearMovAvgModel() {
|
||||
double alpha = randomDouble();
|
||||
double beta = randomDouble();
|
||||
MovAvgModel model = new DoubleExpModel(alpha, beta);
|
||||
MovAvgModel model = new HoltLinearModel(alpha, beta);
|
||||
|
||||
int numValues = randomIntBetween(1, 100);
|
||||
int windowSize = randomIntBetween(1, 50);
|
||||
|
|
@ -250,10 +250,10 @@ public class MovAvgUnitTests extends ElasticsearchTestCase {
|
|||
}
|
||||
|
||||
@Test
|
||||
public void testDoublePredictionModel() {
|
||||
public void testHoltLinearPredictionModel() {
|
||||
double alpha = randomDouble();
|
||||
double beta = randomDouble();
|
||||
MovAvgModel model = new DoubleExpModel(alpha, beta);
|
||||
MovAvgModel model = new HoltLinearModel(alpha, beta);
|
||||
|
||||
int windowSize = randomIntBetween(1, 50);
|
||||
int numPredictions = randomIntBetween(1,50);
|
||||
|
|
|
|||
Loading…
Reference in New Issue