sw function

Generating sliding windows of data

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.

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.

Returns

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

Details

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

Examples

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

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.

Author(s)

Rebecca Pontes Salles