STR: A Seasonal-Trend Decomposition Procedure Based on Regression
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We propose two new general methods for decomposing seasonal time series 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 is related to LASSO. These new methods are more general than any other alternative time series decomposition methods; they allow for multiple seasonal and cyclic components, as well as multiple linear covariates with constant, flexible, seasonal, and cyclic influences. The seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover, they can either be strictly periodic or have a more complex topology. We also provide confidence intervals for the estimated components and discuss how STR can be used for forecasting.
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