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Published in:
2019 | OriginalPaper | Chapter
Methods of Machine Learning for Censored Demand Prediction
Authors : Evgeniy M. Ozhegov, Daria Teterina
Published in: Machine Learning, Optimization, and Data Science
Publisher: Springer International Publishing
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Abstract
In this paper, we analyze a new approach for demand prediction in retail. One of the significant gaps in demand prediction by machine learning methods is the unaccounted sales data censorship. Econometric approaches to modeling censored demand are used to obtain consistent and unbiased estimates of parameters. These approaches can also be transferred to different classes of machine learning models to reduce the prediction error of sales volume. In this study we build two ensemble models to predict demand with and without demand censorship, aggregating predictions for machine learning methods such as Linear regression, Ridge regression, LASSO and Random forest. Having estimated the predictive properties of both models, we test the best predictive power of the models with accounting for the censored nature of demand.