mlm_io: Subset sliding windows of data

In RebeccaSalles/TSPred: Functions for Benchmarking Time Series Prediction

Description

Function subsets sliding windows of data into input and output datasets to be passed to machine-learning methods.

Usage

mlm_io(sw)

Arguments

sw	A numeric matrix with sliding windows of time series data as returned by sw.

Details

When sw has k columns (sliding windows of size k), the input dataset contains the first k-1 columns and the output dataset contains the last column of data.

Value

A list with input and output datasets.

Author(s)

Rebecca Pontes Salles

References

E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrao, G. L. Pappa, and M. Mattoso, 2010, Adaptive Normalization: A novel data normalization approach for non-stationary time series, Proceedings of the International Joint Conference on Neural Networks.

See Also

Other transformation methods: Diff(), LogT(), WaveletT(), emd(), mas(), outliers_bp(), pct(), train_test_subset()

Examples

data(CATS)
swin <- sw(CATS[,1],5)
d <- mlm_io(swin)

RebeccaSalles/TSPred documentation built on April 6, 2021, 2:44 a.m.