stl_features

Computes various measures of trend and seasonality of a time series based on
an STL decomposition. The number of seasonal periods, and the length of the
seasonal periods are returned. Also, the strength of seasonality corresponding
to each period is estimated. The <code>mstl</code> function is used
to do the decomposition.

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

Mitchell O'Hara-Wild

Thiyanga Talagala

Earo Wang

Yangzhuoran Yang

Souhaib Ben Taieb

Cao Hanqing

D K Lake

Nikolay Laptev

J R Moorman

Bohan Zhang

stl_features function

<dl><dt>x</dt>
<dd>a univariate time series.</dd>
<dt>...</dt>
<dd>Other arguments are passed to <code>mstl</code>.</dd></dl>

Arguments

Author

Strength of trend and seasonality of a time series — stl_features

<dl>

<dt>x</dt>
<dd>a univariate time series.</dd>


<dt>...</dt>
<dd>Other arguments are passed to <code>mstl</code>.</dd>

</dl>

Strength of trend and seasonality of a time series

stl_features: Strength of trend and seasonality of a time series

Description

Usage

Value

Arguments

Author