entropy.SlidingWindows: Approximate entropy with sliding windows.

In SlidingWindows: Methods for Time Series Analysis

Description

This function computes approximate entropy of a univariate time series with sliding windows approach.

Usage

entropy.SlidingWindows(y, w = 99, k = 4, dim = 2, r = 0.5, lag = 1)

Arguments

y	A vector containing univariate time series.
w	An integer value indicating the window size w < length(y). If w = length(y), will be computed the function will not slide.
k	An integer value indicating the boundary of the division (N/k). The smallest value of k is 4.
dim	The dimension of given time series. See TSEntropies package.
r	The radius of searched areas. See TSEntropies package.
lag	The downsampling. See TSEntropies package.

Details

This function return the list with time series sliding windows.

Value

A list contaning "w", "ApEn", "FastApEn".

References

Pincus, S.M. (1991). Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA, Vol. 88, pp. 2297–2301. doi="doi.org/10.1073/pnas.88.6.2297".

Examples

y <- rnorm(100)
entropy.SlidingWindows(y, w=99, k=4, dim=2, r=.2,lag=1)

SlidingWindows documentation built on April 11, 2021, 9:07 a.m.