plotStl#

Purpose#

Plot STL decomposition in four panels: original data, trend, seasonal pattern, and remainder.

Format#

plotStl(y, stl)#

Parameters:

y (Nx1 matrix) – Original time series.
stl (struct stlResult) – STL decomposition result from stlDecompose().

Examples#

Airline Passengers Decomposition#

library timeseries;

fname = getGAUSSHome("pkgs/timeseries/examples/data/airline_passengers.csv");
y = loadd(fname, "passengers");

stl = stlDecompose(y, 12);

// 4 stacked panels
plotStl(y, stl);

Save to File#

plotStl(y, stl);
plotSave("stl_decomposition.png", "px", 800 | 800);

Remarks#

Four panels:

Data — the original series (black line)
Trend — long-run level extracted by LOESS (blue line)
Seasonal — repeating periodic pattern with zero line (green line)
Remainder — what’s left after removing trend and seasonal (red line, with zero line)

The decomposition is additive: Data = Trend + Seasonal + Remainder.

Reading the plot:

If the trend is smooth and captures the long-run movement, the seasonal period is correctly specified.
If the seasonal pattern changes amplitude over time, consider a multiplicative decomposition (take logs first).
If the remainder shows patterns or autocorrelation, the decomposition didn’t capture all the structure — the ARIMA model on the remainder should handle it.