repr_feacliptrend: FeaClipTrend representation of time series

In TSrepr: Time Series Representations

Description

The repr_feacliptrend computes representation of time series based on feature extraction from bit-level representations (clipping and trending).

Usage

repr_feacliptrend(x, func, pieces = 2L, order = 4L)

Arguments

x	the numeric vector (time series)
func	the aggregation function for FeaTrend procedure (sumC or maxC)
pieces	the number of parts of time series to split
order	the order of simple moving average

Details

FeaClipTrend combines FeaClip and FeaTrend representation methods. See documentation of these two methods (check See Also section).

Value

the numeric vector of frequencies of features

Author(s)

Peter Laurinec, <[e-mail]>

References

Laurinec P, and Lucka M (2018) Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting. Data Mining and Knowledge Discovery. Springer. DOI: 10.1007/s10618-018-0598-2

See Also

repr_featrend, repr_feaclip

Examples

repr_feacliptrend(rnorm(50), maxC, 2, 4)

TSrepr documentation built on July 13, 2020, 9:07 a.m.