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Automatic Interpretable Retail forecasting with promotional scenarios

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  • Gür Ali, Özden
  • Gürlek, Ragıp

Abstract

Budgeting and planning processes require medium-term sales forecasts with marketing scenarios. The complexity in modern retailing necessitates consistent, automatic forecasting and insight generation. Remedies to the high dimensionality problem have drawbacks; black box machine learning methods require voluminous data and lack insights, while regularization may bias causal estimates in interpretable models.

Suggested Citation

  • Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:4:p:1389-1406
    DOI: 10.1016/j.ijforecast.2020.02.003
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