feat_pacf: Partial autocorrelation-based features

In feasts: Feature Extraction and Statistics for Time Series

Partial autocorrelation-based features

Description

Computes various measures based on partial autocorrelation coefficients of the original series, first-differenced series and second-differenced series.

Usage

feat_pacf(x, .period = 1, lag_max = NULL, ...)

Arguments

x	a univariate time series
.period	The seasonal period (optional)
lag_max	maximum lag at which to calculate the acf. The default is max(.period, 10L) for feat_acf, and max(.period, 5L) for feat_pacf
...	Further arguments passed to stats::acf() or stats::pacf()

Value

A vector of 3 values: Sum of squared of first 5 partial autocorrelation coefficients of the original series, first differenced series and twice-differenced series. For seasonal data, the partial autocorrelation coefficient at the first seasonal lag is also returned.

Author(s)

Thiyanga Talagala

feasts documentation built on Sept. 30, 2024, 9:14 a.m.