Package net.sourceforge.openforecast.models

Defines the different ForecastingModels implemented in OpenForecast.

See:
Description

Class Summary

AbstractForecastingModel	This class implements a variety of methods that are common across all forecasting models.
AbstractTimeBasedModel	A time based forecasting model is the base class that implements much of the common code for models based on a time series.
DoubleExponentialSmoothingModel	Double exponential smoothing - also known as Holt exponential smoothing - is a refinement of the popular simple exponential smoothing model but adds another component which takes into account any trend in the data.
MovingAverageModel	A moving average forecast model is based on an artificially constructed time series in which the value for a given time period is replaced by the mean of that value and the values for some number of preceding and succeeding time periods.
MultipleLinearRegressionModel	Implements a multiple variable linear regression model using the variables named in the constructor as the independent variables, or the variables passed into one of the init methods.
NaiveForecastingModel	A naive forecasting model is a special case of the moving average forecasting model where the number of periods used for smoothing is 1.
PolynomialRegressionModel	Implements a single variable polynomial regression model using the variable named in the constructor as the independent variable.
RegressionModel	Implements a single variable linear regression model using the variable named in the constructor as the independent variable.
SimpleExponentialSmoothingModel	A simple exponential smoothing forecast model is a very popular model used to produce a smoothed Time Series.
TripleExponentialSmoothingModel	Triple exponential smoothing - also known as the Winters method - is a refinement of the popular double exponential smoothing model but adds another component which takes into account any seasonality - or periodicity - in the data.
WeightedMovingAverageModel	A weighted moving average forecast model is based on an artificially constructed time series in which the value for a given time period is replaced by the weighted mean of that value and the values for some number of preceding time periods.

Exception Summary

InsufficientDataException	Represents the case when there is insufficient data available to return a valid forecast value.
ModelNotInitializedException	This exception should be thrown when an attempt is made to use a method in a model that has not been initialized by calling init.

Package net.sourceforge.openforecast.models Description

Defines the different ForecastingModels implemented in OpenForecast. In most cases, it will not be necessary to access any of these classes directly. Instead, use the Forecaster class to obtain a reference to the most appropriate model to use for your data set.