Automatic Interpretable Retail forecasting with promotional scenarios

Özden Gür Ali and Ragıp Gürlek

International Journal of Forecasting, 2020, vol. 36, issue 4, 1389-1406

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.

Keywords: Causality; Decomposition; Marketing; Multivariate time series; Panel data; Machine learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:4:p:1389-1406

DOI: 10.1016/j.ijforecast.2020.02.003

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