max_level_shift: Time series features based on sliding windows

In tsfeatures: Time Series Feature Extraction

Time series features based on sliding windows

Description

Computes feature of a time series based on sliding (overlapping) windows. max_level_shift finds the largest mean shift between two consecutive windows. max_var_shift finds the largest var shift between two consecutive windows. max_kl_shift finds the largest shift in Kulback-Leibler divergence between two consecutive windows.

Usage

max_level_shift(x, width = ifelse(frequency(x) > 1, frequency(x), 10))

max_var_shift(x, width = ifelse(frequency(x) > 1, frequency(x), 10))

max_kl_shift(x, width = ifelse(frequency(x) > 1, frequency(x), 10))

Arguments

x	a univariate time series
width	size of sliding window

Details

Computes the largest level shift and largest variance shift in sliding mean calculations

Value

A vector of 2 values: the size of the shift, and the time index of the shift.

Author(s)

Earo Wang and Rob J Hyndman

tsfeatures documentation built on Aug. 28, 2023, 5:09 p.m.