tsPartition

Training and test indices are generated for time series cross validation.
Generated indices are based on the training windowSize, forecast horizons
and whether a rolling or non-rolling cross validation procedure is desired.

Convenient functions for ensemble forecasts in R combining
approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
thetaf(), nnetar(), stlm(), tbats(), snaive() and arfima() can be combined with equal weights, weights
based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
or cross-validated weights. Cross validation for time series data with user-supplied models
and forecasting functions is also supported to evaluate model accuracy.

David Shaub

forecastHybrid

Convenient Functions for Ensemble Time Series Forecasts

Peter Ellis

tsPartition function

<dl><dt>x</dt>
<dd>A time series</dd>
<dt>rolling</dt>
<dd>Should indices be generated for a rolling or non-rolling procedure?</dd>
<dt>windowSize</dt>
<dd>Size of window for training</dd>
<dt>maxHorizon</dt>
<dd>Maximum forecast horizon</dd></dl>

Arguments

Author

Generate training and test indices for time series cross validation — tsPartition

<dl>

<dt>x</dt>
<dd>A time series</dd>


<dt>rolling</dt>
<dd>Should indices be generated for a rolling or non-rolling procedure?</dd>


<dt>windowSize</dt>
<dd>Size of window for training</dd>


<dt>maxHorizon</dt>
<dd>Maximum forecast horizon</dd>

</dl>

tsPartition: Generate training and test indices for time series cross validation

Description

Usage

Value

Arguments

Author

Examples