A Comparative Study of Machine Learning Frameworks for Demand Forecasting

We built various demand forecasting models to predict product demand for grocery items using Python's deep learning library. The purpose of these predictive models is to compare the performance of different open-source... [ view full abstract ]

We built various demand forecasting models to predict product demand for grocery items using Python's deep learning library. The purpose of these predictive models is to compare the performance of different open-source modeling techniques to predict a time-dependent demand at a store-sku level. These demand forecasting models were developed using Keras and scikit-learn packages and we made comparisons along the following dimensions: 1) predictive performance, 2) runtime, 3) scalability and 4) ease of use. The forecasting models explored in this study are Gradient Boosting, Factorization Machines, and three variations of Deep Neural Networks (DNN). We also explored the effectiveness of categorical embedding layers and sequence-to-sequence type architecture in reducing the errors in the demand forecasts. Our best neural network model is currently placed in the top 1.5 percentile on Kaggle.