  Generating forecasts for time series with multiple seasonal cycles is an
important use-case for many industries nowadays. Accounting for the
multi-seasonal patterns becomes necessary to generate more accurate and
meaningful forecasts in these contexts. In this paper, we propose Long
Short-Term Memory Multi-Seasonal Net (LSTM-MSNet), a decomposition based,
unified prediction framework to forecast time series with multiple seasonal
patterns. The current state of the art in this space are typically univariate
methods, in which the model parameters of each time series are estimated
independently. Consequently, these models are unable to include key patterns
and structures that may be shared by a collection of time series. In contrast,
LSTM-MSNet is a globally trained Long Short-Term Memory network (LSTM), where a
single prediction model is built across all the available time series to
exploit the cross series knowledge in a group of related time series.
Furthermore, our methodology combines a series of state-of-the-art
multiseasonal decomposition techniques to supplement the LSTM learning
procedure. In our experiments, we are able to show that on datasets from
disparate data sources, like e.g. the popular M4 forecasting competition, a
decomposition step is beneficial, whereas in the common real-world situation of
homogeneous series from a single application, exogenous seasonal variables or
no seasonal preprocessing at all are better choices. All options are readily
included in the framework and allow us to achieve competitive results for both
cases, outperforming many state-of-the-art multi-seasonal forecasting methods.
