org.elasticsearch.search.aggregations.pipeline.movavg.models

Class MovAvgModel

java.lang.Object

org.elasticsearch.search.aggregations.pipeline.movavg.models.MovAvgModel

Direct Known Subclasses:

EwmaModel, HoltLinearModel, HoltWintersModel, LinearModel, SimpleModel

public abstract class MovAvgModel
extends Object

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MovAvgModel.AbstractModelParser Abstract class which also provides some concrete parsing functionality.

Constructor Summary

Constructors

Constructor and Description
MovAvgModel()

Method Summary

Methods

Modifier and Type	Method and Description
abstract boolean	canBeMinimized() Returns if the model can be cost minimized.
abstract MovAvgModel	clone() Clone the model, returning an exact copy
protected abstract <T extends Number> double[]	doPredict(Collection<T> values, int numPredictions) Calls to the model-specific implementation which actually generates the predictions
protected double[]	emptyPredictions(int numPredictions) Returns an empty set of predictions, filled with NaNs
boolean	hasValue(int valuesAvailable) Checks to see this model can produce a new value, without actually running the algo.
boolean	minimizeByDefault() Should this model be fit to the data via a cost minimizing algorithm by default?
abstract MovAvgModel	neighboringModel() Generates a "neighboring" model, where one of the tunable parameters has been randomly mutated within the allowed range.
abstract <T extends Number> double	next(Collection<T> values) Returns the next value in the series, according to the underlying smoothing model
<T extends Number> double[]	predict(Collection<T> values, int numPredictions) Predicts the next `n` values in the series.
abstract void	writeTo(StreamOutput out) Write the model to the output stream

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MovAvgModel

public MovAvgModel()

Method Detail

minimizeByDefault

public boolean minimizeByDefault()

Should this model be fit to the data via a cost minimizing algorithm by default?

canBeMinimized

public abstract boolean canBeMinimized()

Returns if the model can be cost minimized. Not all models have parameters which can be tuned / optimized.

neighboringModel

public abstract MovAvgModel neighboringModel()

Generates a "neighboring" model, where one of the tunable parameters has been randomly mutated within the allowed range. Used for minimization

hasValue

public boolean hasValue(int valuesAvailable)

Checks to see this model can produce a new value, without actually running the algo. This can be used for models that have certain preconditions that need to be met in order to short-circuit execution

Parameters:

valuesAvailable - Number of values in the current window of values

Returns:

Returns `true` if calling next() will produce a value, `false` otherwise

next

public abstract <T extends Number> double next(Collection<T> values)

Returns the next value in the series, according to the underlying smoothing model

Type Parameters:

T - Type of numeric

Parameters:

values - Collection of numerics to movingAvg, usually windowed

Returns:

Returns a double, since most smoothing methods operate on floating points

predict

public <T extends Number> double[] predict(Collection<T> values,
                                  int numPredictions)

Predicts the next `n` values in the series.

Type Parameters:

T - Type of numeric

Parameters:

values - Collection of numerics to movingAvg, usually windowed

numPredictions - Number of newly generated predictions to return

Returns:

Returns an array of doubles, since most smoothing methods operate on floating points

doPredict

protected abstract <T extends Number> double[] doPredict(Collection<T> values,
                                    int numPredictions)

Calls to the model-specific implementation which actually generates the predictions

Type Parameters:

T - Type of numeric

Parameters:

values - Collection of numerics to movingAvg, usually windowed

numPredictions - Number of newly generated predictions to return

Returns:

Returns an array of doubles, since most smoothing methods operate on floating points

emptyPredictions

protected double[] emptyPredictions(int numPredictions)

Returns an empty set of predictions, filled with NaNs

Parameters:

numPredictions - Number of empty predictions to generate

writeTo

public abstract void writeTo(StreamOutput out)
                      throws IOException

Write the model to the output stream

Parameters:

out - Output stream

Throws:

IOException

clone

public abstract MovAvgModel clone()

Clone the model, returning an exact copy

Overrides:

clone in class Object