Time Series Feature Extraction
Autocorrelation at lag 9. Included for completion and consistency.
Autocorrelation-based features
ARCH LM Statistic
Convert mts object to list of time series
The autocorrelation feature set from software package hctsa
Converts an input vector into a binarized version from software packag...
CompEngine feature set
Number of crossing points
The distribution feature set from software package hctsa
Points inside a given circular boundary in a 2-d embedding space from ...
Spectral entropy of a time series
Time of first minimum in the autocorrelation function from software pa...
The first zero crossing of the autocorrelation function from software ...
Longest flat spot
Implements fluctuation analysis from software package hctsa
Heterogeneity coefficients
Mode of a data vector from software package hctsa
Parameter estimates of Holt's linear trend method
Hurst coefficient
The first zero crossing of the autocorrelation function of the residua...
Time series features based on tiled windows
Time series features based on sliding windows
Local motifs in a binary symbolization of the time series from softwar...
Nonlinearity coefficient
How median depend on distributional outliers from software package `hc...
Partial autocorrelation-based features
The prediction feature set from software package hctsa
Second Sample Entropy of a time series from software package hctsa
Second Sample Entropy from software package hctsa
The scaling feature set from software package hctsa
Bootstrap-based stationarity measure from software package hctsa
The stationarity feature set from software package hctsa
Standard deviation of the first derivative of the time series from sof...
Strength of trend and seasonality of a time series
Normalized nonlinear autocorrelation, the numerator of the trev functi...
tsfeatures: Time Series Feature Extraction
Time series feature matrix
Unit Root Test Statistics
Simulates a hypothetical walker moving through the time domain from so...
Yahoo server metrics
Proportion of zeros
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.
Useful links