classical_decomposition: Classical Seasonal Decomposition by Moving Averages

In feasts: Feature Extraction and Statistics for Time Series

Classical Seasonal Decomposition by Moving Averages

Description

Decompose a time series into seasonal, trend and irregular components using moving averages. Deals with additive or multiplicative seasonal component.

Usage

classical_decomposition(formula, type = c("additive", "multiplicative"), ...)

Arguments

formula	Decomposition specification (see "Specials" section).
type	The type of seasonal component. Can be abbreviated.
...	Other arguments passed to stats::decompose().

Details

The additive model used is:

Y_t = T_t + S_t + e_t

The multiplicative model used is:

Y_t = T_t\,S_t\, e_t

The function first determines the trend component using a moving average (if filter is NULL, a symmetric window with equal weights is used), and removes it from the time series. Then, the seasonal figure is computed by averaging, for each time unit, over all periods. The seasonal figure is then centered. Finally, the error component is determined by removing trend and seasonal figure (recycled as needed) from the original time series.

This only works well if x covers an integer number of complete periods.

Value

A fabletools::dable() containing the decomposed trend, seasonality and remainder from the classical decomposition.

Specials

season

The season special is used to specify seasonal attributes of the decomposition.

season(period = NULL)

period

The periodic nature of the seasonality. This can be either a number indicating the number of observations in each seasonal period, or text to indicate the duration of the seasonal window (for example, annual seasonality would be "1 year").

Examples

as_tsibble(USAccDeaths) %>%
  model(classical_decomposition(value)) %>%
  components()

as_tsibble(USAccDeaths) %>%
  model(classical_decomposition(value ~ season(12), type = "mult")) %>%
  components()

feasts documentation built on March 31, 2023, 11:49 p.m.