TSPred (version 5.1.1)

sw: Generating sliding windows of data

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)

Value

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

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.

Author

Rebecca Pontes Salles

Details

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

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)