kieker.tools.tslib.forecast.ses
Class SESRForecaster

java.lang.Object
  kieker.tools.tslib.forecast.AbstractForecaster<Double>
      kieker.tools.tslib.forecast.AbstractRForecaster
          kieker.tools.tslib.forecast.ses.SESRForecaster

All Implemented Interfaces:

IForecaster<Double>

public class SESRForecaster
extends AbstractRForecaster

This is one of the forecasters used in the research paper on Self-adaptive workload classification and forecasting for proactive resource provisioning (http://dx.doi.org/10.1002/cpe.3224), authored by Herbst et al.

Since:

1.10

Author:

Nikolas Herbst Generalization of MA by using weights according to the exponential function to give higher weight to more recent values. 1st step: estimation of parameters for weights/exp. function 2nd step: calculation of weighted averages as point forecast

Constructor Summary

SESRForecaster(ITimeSeries<Double> historyTimeseries)

SESRForecaster(ITimeSeries<Double> historyTimeseries, int confidenceLevel)

Method Summary

Methods inherited from class kieker.tools.tslib.forecast.AbstractRForecaster

forecast, removeNullValues

Methods inherited from class kieker.tools.tslib.forecast.AbstractForecaster

getConfidenceLevel, getTsOriginal

Methods inherited from class java.lang.Object

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

Constructor Detail

SESRForecaster

public SESRForecaster(ITimeSeries<Double> historyTimeseries)

Parameters:

historyTimeseries - timeseries used by forecating algo

SESRForecaster

public SESRForecaster(ITimeSeries<Double> historyTimeseries,
                      int confidenceLevel)

Parameters:

historyTimeseries - timeseries used by forecating algo

confidenceLevel - confidenceLevel