dsipts.TimeSeries

class dsipts.TimeSeries(name: str, stacked: bool = False)

Class for generating time series object. If you don’t have any time series you can build one fake timeseries using some helping classes (Categorical for instance).

Parameters:

  • name (str) – name of the series

  • stacked (bool) – if true it is a stacked model

Usage:

For example we can generate a toy timeseries:

  • add a multiplicative categorical feature (weekly)

>>> settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])

  • an additive montly feature (here a year is composed by 5 months)

>>> mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])

  • a spotted categorical variable that happens every 100 days and lasts 1 day

>>> spot = Categorical('spot',100,[7],1,'additive',[10])

>>> ts = TimeSeries('prova')

>>> ts.generate_signal(length = 5000,categorical_variables = [settimana,mese,spot],noise_mean=1,type=0) ##we can add also noise

>>> ts.plot()
__init__(name: str, stacked: bool = False)

Class for generating time series object. If you don’t have any time series you can build one fake timeseries using some helping classes (Categorical for instance).

Parameters:

  • name (str) – name of the series

  • stacked (bool) – if true it is a stacked model

Usage:

For example we can generate a toy timeseries:

  • add a multiplicative categorical feature (weekly)

>>> settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])

  • an additive montly feature (here a year is composed by 5 months)

>>> mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])

  • a spotted categorical variable that happens every 100 days and lasts 1 day

>>> spot = Categorical('spot',100,[7],1,'additive',[10])

>>> ts = TimeSeries('prova')

>>> ts.generate_signal(length = 5000,categorical_variables = [settimana,mese,spot],noise_mean=1,type=0) ##we can add also noise

>>> ts.plot()

Methods

__init__(name[, stacked])

Class for generating time series object.

create_data_loader(data, past_steps, ...[, ...])

Create the dataset for the training/inference step

enrich(dataset, columns)

generate_signal([length, ...])

This will generate a syntetic signal with a selected length, a noise level and some categorical variables.

inference([batch_size, num_workers, ...])

similar to inference_on_set only change is split_params that must contain this keys but using the default can be sufficient: 'past_steps','future_steps','shift','keep_entire_seq_while_shifting','starting_point'

inference_on_set([batch_size, num_workers, ...])

This function allows to get the prediction on a particular set (train, test or validation).

load(model, filename[, load_last, dirpath, ...])

Load a saved model

load_signal(data[, enrich_cat, ...])

This is a crucial point in the data structure. We expect here to have a dataset with time as timestamp.

plot()

Easy way to control the loaded data :returns: figure of the target variables :rtype: plotly.graph_objects._figure.Figure

save(filename)

save the timeseries object

set_model(model[, config, custom_init])

Set the model to train

set_verbose(verbose)

split_for_train([perc_train, perc_valid, ...])

Split the data and create the datasets.

train_model(dirpath, split_params[, ...])

Train the model