Base Model

Base structure for creation of new models

Methods:

calc_error: Estimates error according to SciKit’s regression metrics filter_ts: Returns model’s residuals

class skfore.base_model.base_model

Bases: object

ACF_plot()

PACF_plot()

calc_error(ts, error_function=None, ignore_first=0)

Estimates error according to SciKit’s regression metrics

Args:

ts:

Time series to estimate the model

error_function (None or error function):

Error function whose parameters are real time series and estimated time series. If None, error_function is Sci-Kit learn’s mean squared error

cross_validation(ts, n_splits, error_function=None)

density_plot()

filter_ts(ts, ignore_first=0)

Returns model’s residuals

Args:

ts: Time series to estimate residuals

get_predict_ci(ts, confidence_interval=0.95, iterations=1000)

histogram()

normality()

plot(ts, periods=5, confidence_interval=None, iterations=300)

qq_plot()

set_residuals(residuals)

simulate(ts, periods=5, confidence_interval=0.95, iterations=1000)

test()

Raises error if there are not any of the necessary methods defined

time_plot()