outliers_bp: Outlier removal from sliding windows of data

In TSPred: Functions for Benchmarking Time Series Prediction

Description

Function to perform outlier removal from sliding windows of data. The outliers_bp() function removes windows with extreme values using a method based on Box plots for detecting outliers.

Usage

outliers_bp(data, alpha = 1.5)

Arguments

data	A numeric matrix with sliding windows of time series data as returned by sw.
alpha	The multiplier for the interquartile range used as base for outlier removal. The default is set to 1.5. The value 3.0 is also commonly used to remove only the extreme outliers.

Details

The method applied prune any value smaller than the first quartile minus 1.5 times the interquartile range, and also any value larger than the third quartile plus 1.5 times the interquartile range, that is, all the values that are not in the range [Q1-1.5xIQR, Q3+1.5xIQR] are considered outliers and are consequently removed.

Value

Same as data with outliers removed.

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(), mlm_io(), pct(), train_test_subset()

Examples

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

TSPred documentation built on Jan. 21, 2021, 5:10 p.m.