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Global models have been developed to tackle the challenge of forecasting sets of series
that are related or share similarities, but they have not been developed for heterogeneous datasets.
Various methods of partitioning by relatedness have been introduced to enhance the similarities of
sets, resulting in improved forecasting accuracy but often at the cost of a reduced sample size, which
could be harmful. To shed light on how the relatedness between series impacts the effectiveness
of global models in real-world demand-forecasting problems, we perform an extensive empirical
study using the M5 competition dataset. We examine cross-learning scenarios driven by the product
hierarchy commonly employed in retail planning to allow global models to capture interdependencies
across products and regions more effectively. Our findings show that global models outperform
state-of-the-art local benchmarks by a considerable margin, indicating that they are not inherently
more limited than local models and can handle unrelated time-series data effectively. The accuracy
of data-partitioning approaches increases as the sizes of the data pools and the models’ complexity
decrease. However, there is a trade-off between data availability and data relatedness. Smaller
data pools lead to increased similarity among time series, making it easier to capture cross-product
and cross-region dependencies, but this comes at the cost of a reduced sample, which may not
be beneficial. Finally, it is worth noting that the successful implementation of global models for
heterogeneous datasets can significantly impact forecasting practice.
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Global models Deep learning Data partitioning Intermittent demand Time-series features Model complexity Retail