sw: Generating sliding windows of data

In TSPred: Functions for Benchmarking Time Series Prediction

Description

The function extracts all possible subsequences (of the same length) of a time series (or numeric vector), generating a set of sliding windows of data, often used to train machine learning methods.

Usage

sw(x, k)

Arguments

x	A vector or univariate time series from which the sliding windows are to be extracted.
k	Numeric value corresponding to the required size (length) of each sliding window.

Details

The function returns all (overlapping) subsequences of size swSize of timeseries.

Value

A numeric matrix of size (length(x)-k+1) by k, where each line is a sliding window.

Author(s)

Rebecca Pontes Salles

References

Lampert, C. H., Blaschko, M. B., and Hofmann, T. (2008). Beyond sliding windows: Object localization by efficient subwindow search. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.

Keogh, E. and Lin, J. (2005). Clustering of time series subsequences is meaningless: Implications for previous and future research. Knowledge and Information Systems, 8(2):154-177.

Examples

data("CATS")
s <- sw(CATS[,1],4)

TSPred documentation built on Jan. 21, 2021, 5:10 p.m.