pyhctsa.operations.model_fit.fit_subsegments¶

pyhctsa.operations.model_fit.fit_subsegments(y, model='ar', order=2, subset_how='uniform', sample_p=[20, 0.1])¶

Robustness of model parameters across different segments of a time series.

The spread of parameters obtained (including in-sample goodness of fit statistics) provides some indication of stationarity. Values of goodness of fit provide some indication of model suitability.

Parameters:¶

y : array-like¶

The input time series.

model : str, optional¶

The model to fit in each segment of the time series:

• ’ar’: fits an AR model of a specified order. Outputs are how the fitted AR parameters vary across the different segments of time series.

• ’arma’: Not yet implemented.

• ’ss’: Not yet implemented.

order : int, optional¶

The order of the model to fit (used for ‘ar’, ‘ss’, or ‘arma’ models).

subset_how : str, optional¶

How to choose segments from the time series, either:

• ’uniform’ (uniformly)

• ’rand’ (at random) [not implemented].

sample_p : list or tuple, optional¶

A two-vector specifying how many segments to take and of what length. Of the form [n_samples, length], where length can be a proportion of the time-series length. For example, [20, 0.1] takes 20 segments of 10% the time-series length.

Returns:¶

Dictionary of statistics on the spread and mean of fitted model parameters and goodness of fit across segments.

Return type:¶

dict