| sw {TSPred} | R Documentation |
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)
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)