pyFTS.models.incremental package¶

Module contents¶

FTS methods with incremental/online learning

Submodules¶

pyFTS.models.incremental.TimeVariant module¶

Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data

class pyFTS.models.incremental.TimeVariant.Retrainer(**kwargs)¶

Bases: pyFTS.common.fts.FTS

Meta model for incremental/online learning that retrain its internal model after data windows controlled by the parameter ‘batch_size’, using as the training data a window of recent lags, whose size is controlled by the parameter ‘window_length’.

forecast(data, **kwargs)¶

Point forecast one step ahead

Parameters:	data – time series data with the minimal length equal to the max_lag of the model kwargs – model specific parameters
Returns:	a list with the forecasted values

forecast_ahead(data, steps, **kwargs)¶

Point forecast n steps ahead

Parameters:	data – time series data with the minimal length equal to the max_lag of the model steps – the number of steps ahead to forecast (default: 1) start_at – in the multi step forecasting, the index of the data where to start forecasting (default: 0)
Returns:	a list with the forecasted values

offset()¶

Returns the number of lags to skip in the input test data in order to synchronize it with the forecasted values given by the predict function. This is necessary due to the order of the model, among other parameters.

Returns:	An integer with the number of lags to skip

train(data, **kwargs)¶

Method specific parameter fitting

Parameters:	data – training time series data kwargs – Method specific parameters

pyFTS.models.incremental.IncrementalEnsemble module¶

Time Variant/Incremental Ensemble of FTS methods

class pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS(**kwargs)¶

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS