  The decomposition of time series into components is an important task that
helps to understand time series and can enable better forecasting. Nowadays,
with high sampling rates leading to high-frequency data (such as daily, hourly,
or minutely data), many real-world datasets contain time series data that can
exhibit multiple seasonal patterns. Although several methods have been proposed
to decompose time series better under these circumstances, they are often
computationally inefficient or inaccurate. In this study, we propose Multiple
Seasonal-Trend decomposition using Loess (MSTL), an extension to the
traditional Seasonal-Trend decomposition using Loess (STL) procedure, allowing
the decomposition of time series with multiple seasonal patterns. In our
evaluation on synthetic and a perturbed real-world time series dataset,
compared to other decomposition benchmarks, MSTL demonstrates competitive
results with lower computational cost. The implementation of MSTL is available
in the R package forecast.
