smooth (version 4.1.1)

msdecompose: Multiple seasonal classical decomposition

Description

Function decomposes multiple seasonal time series into components using the principles of classical decomposition.

Usage

msdecompose(y, lags = c(12), type = c("additive", "multiplicative"),
  smoother = c("ma", "lowess", "supsmu"), ...)

Value

The object of the class "msdecompose" is return, containing:

• y - the original time series.

• initial - the estimates of the initial level and trend.

• trend - the long term trend in the data.

• seasonal - the list of seasonal parameters.

• lags - the provided lags.

• type - the selected type of the decomposition.

• yName - the name of the provided data.

Arguments

y

Vector or ts object, containing data needed to be smoothed.

lags

Vector of lags, corresponding to the frequencies in the data.

type

The type of decomposition. If "multiplicative" is selected, then the logarithm of data is taken prior to the decomposition.

smoother

The type of function used in the smoother of the data to extract the trend and in seasonality smoothing. smoother="ma" relies on the centred moving average and will result in the classical decomposition. smoother="lowess" will use lowess, resulting in a decomposition similar to the STL (stl). Finally, smoother="supsmu" will use the Friedman's super smoother via supsmu.

...

Other parameters passed to smoothers. Only works with lowess/supsmu.

Author

Ivan Svetunkov,

Details

The function applies centred moving averages based on filter function and order specified in lags variable in order to smooth the original series and obtain level, trend and seasonal components of the series.

References

• Svetunkov I. (2023) Smooth forecasting with the smooth package in R. arXiv:2301.01790. tools:::Rd_expr_doi("10.48550/arXiv.2301.01790").

• Svetunkov I. (2015 - Inf) "smooth" package for R - series of posts about the underlying models and how to use them: https://openforecast.org/category/r-en/smooth/.

See Also

filter

Examples

# Decomposition of multiple frequency data
if (FALSE) ourModel <- msdecompose(forecast::taylor, lags=c(48,336), type="m")
ourModel <- msdecompose(AirPassengers, lags=c(12), type="m")

plot(ourModel)
plot(forecast(ourModel, model="AAN", h=12))