Retail Demand Forecasting with Machine Learning

Retail Demand Forecasting with Machine Learning: For over two decades, time-series methods, in combination with hierarchical spreading/aggregation via location and product hierarchies, and subsequent manual user adjustments, have been a standard means by which retailers and the software vendors who serve them have created demand forecasts. The forecasts so produced are and were used as inputs to store and vendor replenishment, regular and markdown pricing, and other downstream decision support systems. The rise of machine learning — the advent of high-powered commercial product recommender systems such as books at amazon book and movies at netflix, of powerful search (e.g., google), text processing (e.g., Facebook) and sentiment analysis capabilities, IBM Watson, self-driving cars and the like — is real phenomenon based on academically-sound and industrially-proven techniques whose application to retail demand forecasting is ripe.

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