Seasonal Trend Decomposition Using Regression

Methods for decomposing seasonal data: STR (a Seasonal-Trend time series decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting.

stR

The goal of stR is to provide two methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can also be used for forecasting.

Installation

You can install the release version from CRAN.

install.packages('stR')

You can install the development version from GitHub.

# install.packages("remotes")
devtools::install_github("robjhyndman/stR")

Example

For most users, the AutoSTR() function will be the preferred way of using the package.

library(stR)

# Decomposition of a multiple seasonal time series
decomp <- AutoSTR(calls)
plot(decomp)

# Decomposition of a monthly time series
decomp <- AutoSTR(log(grocery))
plot(decomp)

See the vignette for more advanced options.