motiftwo_entro3

<p>Coarse-graining is performed. Time-series values above its mean are given 1,
and those below the mean are 0.</p>

Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

Rob Hyndman

tsfeatures

Time Series Feature Extraction

Yanfei Kang

Pablo Montero-Manso

Thiyanga Talagala

Earo Wang

Yangzhuoran Yang

Mitchell O'Hara-Wild

Souhaib Ben Taieb

Cao Hanqing

D K Lake

Nikolay Laptev

J R Moorman

motiftwo_entro3 function

Local motifs in a binary symbolization of the time series from software package <code>hctsa</code> — motiftwo_entro3

Local motifs in a binary symbolization of the time series from software package <code>hctsa</code>

motiftwo_entro3: Local motifs in a binary symbolization of the time series from software package `hctsa`

Description

Usage

Arguments

Value

References

Examples