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Methods of Machine Learning for Censored Demand Prediction

Student: Semenova Daria

Supervisor: Evgeniy M. Ozhegov

Faculty: Faculty of Economics, Management, and Business Informatics

Educational Programme: Economics (Bachelor)

Final Grade: 10

Year of Graduation: 2018

In this paper, we analyze a new approach for demand prediction in retail taking into account data censorship and using machine learning methods. One of the significant gaps in demand prediction by machine learning methods is the unaccounted 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 future prices or sales volumes. In this study we build two ensemble demand models with and without demand censorship, aggregating predictions for machine learning methods such as Ridge regression, LASSO and Random Forest. Having estimated the predictive properties of both models, we empirically prove the best predictive power of the model, taking into account the censored nature of demand.

Full text (added May 18, 2018)

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