| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 540.87 KB | Adobe PDF |
Advisor(s)
Abstract(s)
: Retailers must have accurate sales forecasts to efficiently and effectively operate their
businesses and remain competitive in the marketplace. Global forecasting models like RNNs can
be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated
and influenced by a variety of external factors. By including covariates in a forecasting model, we
can often better capture the various factors that can influence sales in a retail setting. This can help
improve the accuracy of our forecasts and enable better decision making for inventory management,
purchasing, and other operational decisions. In this study, we investigate how the accuracy of global
forecasting models is affected by the inclusion of different potential demand covariates. To ensure the
significance of the study’s findings, we used the M5 forecasting competition’s openly accessible and
well-established dataset. The results obtained from DeepAR models trained on different combinations
of features indicate that the inclusion of time-, event-, and ID-related features consistently enhances
the forecast accuracy. The optimal performance is attained when all these covariates are employed
together, leading to a 1.8% improvement in RMSSE and a 6.5% improvement in MASE compared to
the baseline model without features. It is noteworthy that all DeepAR models, both with and without
covariates, exhibit a significantly superior forecasting performance in comparison to the seasonal
naïve benchmark.
Description
Keywords
Deep neural networks Covariates Time series forecasting Retailing