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_lag()

Lag plots

gg_arma()

Plot characteristic ARMA roots

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

X11()

X11 seasonal decomposition

SEATS()

Seasonal decomposition with X-13ARIMA-SEATS

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 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

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

stat_arch_lm()

ARCH LM Statistic

n_crossing_points()

Number of crossing points

n_flat_spots()

Number of flat spots

coef_hurst()

Hurst coefficient

guerrero()

Guerrero's method for Box Cox lambda selection