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)[source]¶

Bases: pyFTS.common.fts.FTS

Meta model for incremental/online learning

auto_update = None¶: If true the model is updated at each time and not recreated

batch_size = None¶: The batch interval between each retraining

forecast(data, **kwargs)[source]¶

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

fts_method = None¶: The FTS method to be called when a new model is build

fts_params = None¶: The FTS method specific parameters

model = None¶: The most recent trained model

partitioner = None¶: The most recent trained partitioner

partitioner_method = None¶: The partitioner method to be called when a new model is build

partitioner_params = None¶: The partitioner method parameters

train(data, **kwargs)[source]¶

Method specific parameter fitting

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

window_length = None¶: The memory window length

pyFTS.models.incremental.IncrementalEnsemble module¶

Time Variant/Incremental Ensemble of FTS methods

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

Bases: pyFTS.models.ensemble.ensemble.EnsembleFTS