Overview 


feasts: Feature Extraction and Statistics for Time Series 

GraphicsVisualisation is often the first step in understanding the patterns in time series data. The package uses ggplot2 to produce customisable graphics to visualise time series patterns. 

Auto and Cross Covariance and Correlation plots 

Seasonal plot 

Seasonal subseries plots 

Ensemble of time series displays 

Ensemble of time series residual diagnostic plots 

Lag plots 

Plot characteristic ARMA roots 

DecompositionsUseful for decomposing a time series into some simpler structural components. 

Classical Seasonal Decomposition by Moving Averages 

Multiple seasonal decomposition by Loess 

Generate block bootstrapped series from an STL decomposition 

X13ARIMASEATS Seasonal Adjustment 

Autocorrelation analysisIdentify autocorrelations in the data. 

(Partial) Autocorrelation and CrossCorrelation Function Estimation 

Autocorrelationbased features 

Partial autocorrelationbased features 

Unit root testsUnit root tests for use with 

Unit root tests 

Number of differences required for a stationary series 

Portmanteau testsStatistical tests for examining the null hypothesis of independence in a given time series. 

Portmanteau tests 

Tiling window featuresComputes feature of a time series based on tiled (nonoverlapping) windows. 

Time series features based on tiled windows 

Sliding window featuresComputes feature of a time series based on sliding (overlapping) windows. 

Sliding window features 

Other featuresUncategorised features 

STL features 

Spectral features of a time series 

Intermittency features 

ARCH LM Statistic 

Number of crossing points 

Longest flat spot length 

Hurst coefficient 

Guerrero's method for Box Cox lambda selection 