Convenient Functions for Ensemble Time Series Forecasts
Accuracy measures for cross-validated time series
Accuracy measures for hybridModel objects
Validate that CV window parameters are valid
Helper function to test all the model arguments (e.g. a.args, e.args, ...
Helper function to check the that the parallel arguments are valid
Cross validation for time series
Extract cross validated rolling forecasts
Extract Model Fitted Values
Hybrid forecast
Forecast using a Theta model
Return a forecast model function for a given model character
Translate character to model name
Hybrid time series modeling
Test if the object is a hybridModel object
Plot a hybridModel object
Plot components from Theta model
Plot the fitted values of a hybridModel object
Plot the component models of a hybridModel object
Helper function to validate and clean the input time series
Print information about the hybridModel object
Helper function to remove models that require more data
Extract Model Residuals
Print a summary of the hybridModel object
Theta method 'model'
Forecast ensemble using THieF
Combine multiple sequential time series
Generate training and test indices for time series cross validation
Subset time series with provided indices
Helper function used to unpack the fitted model objects from a list
Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() 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.
Useful links