Package index • feasts

Overview
`feasts` `feasts-package`	feasts: Feature Extraction and Statistics for Time Series
Graphics Visualisation 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.
`autoplot(<tbl_cf>)`	Auto- and Cross- Covariance and -Correlation plots
`gg_season()`	Seasonal plot
`gg_subseries()`	Seasonal subseries plots
`gg_tsdisplay()`	Ensemble of time series displays
`gg_tsresiduals()`	Ensemble of time series residual diagnostic plots
`gg_lag()`	Lag plots
`gg_arma()`	Plot characteristic ARMA roots
`gg_irf()`	Plot impulse response functions
Decompositions Useful for decomposing a time series into some simpler structural components.
`classical_decomposition()`	Classical Seasonal Decomposition by Moving Averages
`STL()`	Multiple seasonal decomposition by Loess
`generate(<stl_decomposition>)`	Generate block bootstrapped series from an STL decomposition
`X_13ARIMA_SEATS()`	X-13ARIMA-SEATS Seasonal Adjustment
Autocorrelation analysis Identify autocorrelations in the data.
`ACF()` `PACF()` `CCF()`	(Partial) Autocorrelation and Cross-Correlation Function Estimation
`feat_acf()`	Autocorrelation-based features
`feat_pacf()`	Partial autocorrelation-based features
Unit root tests Unit root tests for use with `fabletools::features()`.
`unitroot_kpss()` `unitroot_pp()`	Unit root tests
`unitroot_ndiffs()` `unitroot_nsdiffs()`	Number of differences required for a stationary series
Portmanteau tests Statistical tests for examining the null hypothesis of independence in a given time series.
`ljung_box()` `box_pierce()` `portmanteau_tests`	Portmanteau tests
Cointegration tests Statistical tests for investigating cointegration between time series.
`cointegration_johansen()`	Johansen Procedure for VAR
`cointegration_phillips_ouliaris()`	Phillips and Ouliaris Cointegration Test
Tiling window features Computes feature of a time series based on tiled (non-overlapping) windows.
`var_tiled_var()` `var_tiled_mean()`	Time series features based on tiled windows
Sliding window features Computes feature of a time series based on sliding (overlapping) windows.
`shift_level_max()` `shift_var_max()` `shift_kl_max()`	Sliding window features
Other features Uncategorised features
`feat_stl()`	STL features
`feat_spectral()`	Spectral features of a time series
`feat_intermittent()`	Intermittency features
`stat_arch_lm()`	ARCH LM Statistic
`n_crossing_points()`	Number of crossing points
`longest_flat_spot()`	Longest flat spot length
`coef_hurst()`	Hurst coefficient
`guerrero()`	Guerrero's method for Box Cox lambda selection

Reference

Overview

Graphics

Decompositions

Autocorrelation analysis

Unit root tests

Portmanteau tests

Cointegration tests

Tiling window features

Sliding window features

Other features