extract_tsfeatures: Extract features from a collection of time series
In oddstream: Outlier Detection in Data Streams

Description Usage Arguments Value References See Also Examples

View source: R/extract_tsfeatures.R

This function extract time series features from a collection of time series. This is a modification oftsmeasures function of anomalous package package .

1 2	extract_tsfeatures(y, normalise = TRUE, width = ifelse(frequency(y) > 1, frequency(y), 10), window = width)

`y`	A multivariate time serie
`normalise`	If TRUE, each time series is scaled to be normally distributed with mean 0 and sd 1
`width`	A window size for variance change, level shift and lumpiness
`window`	A window size for KLscore

An object of class features with the following components:

`mean`	Mean
`variance`	Variance
`lumpiness`	Variance of annual variances of remainder
`lshift`	Level shift using rolling window
`vchange`	Variance change
`linearity`	Strength of linearity
`curvature`	Strength of curvature
`spikiness`	Strength of spikiness
`season`	Strength of seasonality
`peak`	Strength of peaks
`trough`	Strength of trough
`BurstinessFF`	Burstiness of time series using Fano Factor
`minimum`	Minimum value
`maximum`	Maximum value
`rmeaniqmean`	Ratio between interquartile mean and the arithmetic mean
`moment3`	Third moment
`highlowmu`	Ratio between the means of data that is below and upper the global mean

Hyndman, R. J., Wang, E., & Laptev, N. (2015). Large-scale unusual time series detection. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW), (pp. 1616-1619). IEEE.

Fulcher, B. D. (2012). Highly comparative time-series analysis. PhD thesis, University of Oxford.

find_odd_streams, get_pc_space, set_outlier_threshold, gg_featurespace

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mvtsplot::mvtsplot(anomalous_stream, levels=8, gcol=2, norm="global")
features <- extract_tsfeatures(anomalous_stream[500:550, ])
plot.ts(features[, 1:10])