slidingWindows-deprecated: Generating sliding windows of data
In TSPred: Functions for Benchmarking Time Series Prediction
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
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
1
slidingWindows(timeseries, swSize)
Arguments
timeseries
A vector or univariate time series from which the sliding
windows are to be extracted.
swSize
Numeric value of 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(timeseries)-swSize+1)
by swSize, 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.
See Also
TSPred documentation built on Jan. 21, 2021, 5:10 p.m.
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Description Usage Arguments Details Value Author(s) References See Also
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
1 | slidingWindows(timeseries, swSize)
|
Arguments
timeseries |
A vector or univariate time series from which the sliding windows are to be extracted. |
swSize |
Numeric value of 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(timeseries)-swSize+1)
by swSize, 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.
See Also
R Package Documentation
Browse R Packages
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