tsfeatures: heterogeneity – R documentation

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heterogeneity

Heterogeneity coefficients

Description

Computes various measures of heterogeneity of a time series. First the series is pre-whitened using an AR model to give a new series y. We fit a GARCH(1,1) model to y and obtain the residuals, e. Then the four measures of heterogeneity are: (1) the sum of squares of the first 12 autocorrelations of y^2; (2) the sum of squares of the first 12 autocorrelations of e^2; (3) the R^2 value of an AR model applied to y^2; (4) the R^2 value of an AR model applied to e^2. The statistics obtained from y^2 are the ARCH effects, while those from e^2 are the GARCH effects.

Usage

heterogeneity(x)

Arguments

`x`	a univariate time series

Value

A vector of numeric values.

Author(s)

Yanfei Kang and Rob J Hyndman

tsfeatures

Time Series Feature Extraction

v1.0.2

GPL-3

Authors

Rob Hyndman [aut, cre] (<https://orcid.org/0000-0002-2140-5352>), Yanfei Kang [aut] (<https://orcid.org/0000-0001-8769-6650>), Pablo Montero-Manso [aut], Thiyanga Talagala [aut] (<https://orcid.org/0000-0002-0656-9789>), Earo Wang [aut] (<https://orcid.org/0000-0001-6448-5260>), Yangzhuoran Yang [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Souhaib Ben Taieb [ctb], Cao Hanqing [ctb], D K Lake [ctb], Nikolay Laptev [ctb], J R Moorman [ctb]

Initial release