com.yahoo.egads.models.tsmm

Class TimeSeriesAbstractModel

java.lang.Object

com.yahoo.egads.models.tsmm.TimeSeriesAbstractModel

All Implemented Interfaces:

JsonAble, Model, TimeSeriesModel, java.io.Serializable

Direct Known Subclasses:

AutoForecastModel, DoubleExponentialSmoothingModel, MovingAverageModel, MultipleLinearRegressionModel, NaiveForecastingModel, NullModel, OlympicModel, OlympicModel2, PolynomialRegressionModel, RegressionModel, SimpleExponentialSmoothingModel, SpectralSmoother, TripleExponentialSmoothingModel, WeightedMovingAverageModel

public abstract class TimeSeriesAbstractModel
extends java.lang.Object
implements TimeSeriesModel

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
TimeSeriesAbstractModel(java.util.Properties config)

Method Summary

Modifier and Type	Method and Description
static boolean	betterThan(TimeSeriesAbstractModel model1, TimeSeriesAbstractModel model2)
void	fromJson(JSONObject json_obj)
double	getBias() Returns the bias - the arithmetic mean of the errors - obtained from applying the current forecasting model to the initial data set to try and predict each data point.
double	getMAD() Returns the mean absolute deviation obtained from applying the current forecasting model to the initial data set to try and predict each data point.
double	getMAPE() Returns the mean absolute percentage error obtained from applying the current forecasting model to the initial data set to try and predict each data point.
java.lang.String	getModelName()
java.lang.String	getModelType()
double	getMSE() Returns the mean square of the errors (MSE) obtained from applying the current forecasting model to the initial data set to try and predict each data point.
double	getSAE() Returns the Sum of Absolute Errors (SAE) obtained by applying the current forecasting model to the initial data set.
void	toJson(JSONStringer json_out)

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.yahoo.egads.models.tsmm.TimeSeriesModel

predict, train, update

Methods inherited from interface com.yahoo.egads.data.Model

reset

Constructor Detail

TimeSeriesAbstractModel

public TimeSeriesAbstractModel(java.util.Properties config)

Method Detail

getModelName

public java.lang.String getModelName()

Specified by:

getModelName in interface Model

getModelType

public java.lang.String getModelType()

Specified by:

getModelType in interface Model

toJson

public void toJson(JSONStringer json_out)
            throws java.lang.Exception

Specified by:

toJson in interface JsonAble

Throws:

java.lang.Exception

fromJson

public void fromJson(JSONObject json_obj)
              throws java.lang.Exception

Specified by:

fromJson in interface JsonAble

Throws:

java.lang.Exception

betterThan

public static boolean betterThan(TimeSeriesAbstractModel model1,
                                 TimeSeriesAbstractModel model2)

getBias

public double getBias()

Returns the bias - the arithmetic mean of the errors - obtained from applying the current forecasting model to the initial data set to try and predict each data point. The result is an indication of the accuracy of the model when applied to your initial data set - the smaller the bias, the more accurate the model.

Returns:

the bias - mean of the errors - when the current model was applied to the initial data set.

getMAD

public double getMAD()

Returns the mean absolute deviation obtained from applying the current forecasting model to the initial data set to try and predict each data point. The result is an indication of the accuracy of the model when applied to your initial data set - the smaller the Mean Absolute Deviation (MAD), the more accurate the model.

Returns:

the mean absolute deviation (MAD) when the current model was applied to the initial data set.

getMAPE

public double getMAPE()

Returns the mean absolute percentage error obtained from applying the current forecasting model to the initial data set to try and predict each data point. The result is an indication of the accuracy of the model when applied to the initial data set - the smaller the Mean Absolute Percentage Error (MAPE), the more accurate the model.

Returns:

the mean absolute percentage error (MAPE) when the current model was applied to the initial data set.

getMSE

public double getMSE()

Returns the mean square of the errors (MSE) obtained from applying the current forecasting model to the initial data set to try and predict each data point. The result is an indication of the accuracy of the model when applied to your initial data set - the smaller the Mean Square of the Errors, the more accurate the model.

Returns:

the mean square of the errors (MSE) when the current model was applied to the initial data set.

getSAE

public double getSAE()

Returns the Sum of Absolute Errors (SAE) obtained by applying the current forecasting model to the initial data set. Initialized following a call to init.

Returns:

the sum of absolute errors (SAE) obtained by applying this forecasting model to the initial data set.