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Default Forecast

Synopsis

This operator trains a Default Forecast model on time series data.

Description

The Default Forecast model performs the forecast by predicting the sameforecast valuefor the whole horizon window. It can be used to test a forecasting method (e.g. ARIMA, Holt-Winters, Windowing) against a baseline forecasting performance, provided by this Default Forecast model.

The method to determine theforecast valuecan be selected by the parametermethod. The last value of the time series can be used or themean in window,median in windowormode in window. For the later three methods, the window always consists of the lastnvalues of the time series, withnspecified by the user (see parameterwindow size). By default invalid values (missing, positive and negative infinity for numerical time series and emtpy strings for nominal time series) are included in the calculation ofmean,median, ormode. If the parameterignore invalid valuesis selected, the invalid values are ignored in the calculation.

Themodeis derived as the most frequent value in the window. If more than one value have the highest frequency the value which occurs first in the window is used. Themethodsmean in windowandmedian in windowcan only be used for numerical series, while the others work also for nominal and time series with date time values. For more details about the calculation ofmean,median, ormodesee the operator help of the operatorsExtract AggregateandExtract Mode.

This operator works on all time series (numerical, nominal and time series with date time values) for themethodslast valueandmode in window. Themethodsmean in windowandmedian in windowonly work on numerical time series.

Differentiation

This operator is similar to other modeling operators, but is specifically designed to work on time series data. One of the implications of this is, that the forecast model should be applied on the same data it was trained on.

Apply Forecast

This operator receives a trained Forecast Model (e.g. the Default Forecast model) and creates the forecast for the time series it was trained on.

ARIMA

This operator trains an ARIMA model (Autoregressive Integrated Moving Average) on time series data to perform a forecast.

Function and Seasonal Component Forecast

This operator trains a Function and Seasonal Forecast model (combining fitted function and seasonal component values) on time series data to perform a forecast.

Holt-Winters

This operator trains a Holt-Winters model (triple exponential smoothing) on time series data to perform a forecast.

Input

example set

The ExampleSet which contains the time series data as an attribute.

Output

预测模型

The Default Forecast model calculated from the specified time series attribute. It also contains the original time series values.

original

The ExampleSet that was given as input is passed through without changes.

Parameters

Time series attribute

The time series attribute for which the Default Forecast model should be build. The required attribute can be selected from this option. The attribute name can be selected from the drop down box of the parameter if the meta data is known.

Has indices

This parameter indicates if there is an index attribute associated with the time series. If this parameter is set to true, the index attribute has to be selected.

Indices attribute

If the parameterhas indicesis set to true, this parameter defines the associated index attribute. It can be either a date, date_time or numeric value type attribute. The attribute name can be selected from the drop down box of the parameter if the meta data is known.

Method

这个参数specifies the method to calculate theforecast valueof the Default Forecast model.

  • last value: The last value of the time series is used.
  • mean in window: Mean of the values in the windowed time series. If invalid values aren't ignored, the mean is missing if any time series value is missing, positive or negative infinity.
  • median in window: Median of the values in the windowed time series. If invalid values aren't ignored, these values are listed in the same way as finite values for the determination of the median.
  • mode in window: Mode (most frequent) of the values in the windowed time series. If more than one value have the highest frequency the value which occurs first in the window is used. If invalid values aren't ignored and an invalid value is the most frequent in a time series, the computed mode is this value.

窗口大小

这个参数specifies how many values are used in the calculation ofmean,medianormodeof the window. The window are always the lastnvalues in the time series, n is specified by this parameter.

Ignore invalid values

If this parameter is set to true invalid values (missing for all time series, positive infinity and negative infinity for numeric time series and empty strings for nominal time series) are ignored in the calculation of themean,medianormodein the window.