3.6 STL decomposition
STL is a versatile and robust method for decomposing time series. STL is an acronym for “Seasonal and Trend decomposition using Loess”, while loess is a method for estimating nonlinear relationships. The STL method was developed by R. B. Cleveland et al. (1990).
STL has several advantages over classical decomposition, and the SEATS and X-11 methods:
- Unlike SEATS and X-11, STL will handle any type of seasonality, not only monthly and quarterly data.
- The seasonal component is allowed to change over time, and the rate of change can be controlled by the user.
- The smoothness of the trend-cycle can also be controlled by the user.
- It can be robust to outliers (i.e., the user can specify a robust decomposition), so that occasional unusual observations will not affect the estimates of the trend-cycle and seasonal components. They will, however, affect the remainder component.
On the other hand, STL has some disadvantages. In particular, it does not handle trading day or calendar variation automatically, and it only provides facilities for additive decompositions.
A multiplicative decomposition can be obtained by first taking logs of the data, then back-transforming the components. Decompositions that are between additive and multiplicative can be obtained using a Box-Cox transformation of the data with \(0<\lambda<1\). A value of \(\lambda=0\) gives a multiplicative decomposition while \(\lambda=1\) gives an additive decomposition.
The best way to begin learning how to use STL is to see some examples and experiment with the settings. Figure 3.7 showed an example of an STL decomposition applied to the total US retail employment series. Figure 3.18 shows an alternative STL decomposition where the trend-cycle is more flexible, the seasonal pattern is fixed, and the robust option has been used.
us_retail_employment |>
model(
STL(Employed ~ trend(window = 7) +
season(window = "periodic"),
robust = TRUE)) |>
components() |>
autoplot()
Figure 3.18: Total US retail employment (top) and its three additive components obtained from a robust STL decomposition with flexible trend-cycle and fixed seasonality.
The two main parameters to be chosen when using STL are the trend-cycle window trend(window = ?) and the seasonal window season(window = ?). These control how rapidly the trend-cycle and seasonal components can change. Smaller values allow for more rapid changes. Both trend and seasonal windows should be odd numbers; trend window is the number of consecutive observations to be used when estimating the trend-cycle; season window is the number of consecutive years to be used in estimating each value in the seasonal component. Setting the seasonal window to be infinite is equivalent to forcing the seasonal component to be periodic season(window='periodic') (i.e., identical across years). This was the case in Figure 3.18.
By default, the STL() function provides a convenient automated STL decomposition using a seasonal window of season(window=13), and the trend window chosen automatically from the seasonal period. The default setting for monthly data is trend(window=21). This usually gives a good balance between overfitting the seasonality and allowing it to slowly change over time. But, as with any automated procedure, the default settings will need adjusting for some time series. In this case the default trend window setting produces a trend-cycle component that is too rigid. As a result, signal from the 2008 global financial crisis has leaked into the remainder component, as can be seen in the bottom panel of Figure 3.7. Selecting a shorter trend window as in Figure 3.18 improves this.