Amazon Technologies, Inc.

Kari E J Torkkola

Balakrishnan Narayanaswamy

Fedor Zhdanov

Zhihao Cen

Wen-Yu Hua

Alexander Matthew Lamb

Ru He

Techniques described herein include a method and system for item demand forecasting that utilizes machine learning techniques to generate a set of quantiles. In some embodiments, several item features may be identified as being relevant to an item forecast and may be provided as inputs to a regression module, which may calculate a set of quantiles for each item. A set of quantiles may comprise a number of confidence levels or probabilities associated with calculated demand values for an item. In some embodiments, costs associated with the item may be used to select an appropriate quantile associated (e.g., based on a corresponding confidence level). In some embodiments, an item demand forecast may be generated based on the calculated demand value associated with the selected quantile. In some embodiments, one or more of the item may be automatically ordered based on that item demand forecast.

Demand forecasting via direct quantile loss optimization

Methods and systems for forecasting demand are described. A method may include determining a demand pattern for each respective item of at least some items of a plurality of items. The method may also include clustering the plurality of items into a plurality of clusters based on the determined demand patterns. The method may further include determining a composite demand pattern for each cluster. The method may additionally include forecasting a demand for an item of the plurality of items based on the composite demand pattern of the cluster to which the item belongs.

Cluster-based demand forecasting procedure

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Seattle, WA, United States of America

Kari E J Torkkola