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In our experiments, we are able to show that on data sets from disparate data sources, 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 multiseasonal forecasting methods.Industrial big d
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