A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods

Abstract

Demand forecasting is a fundamental aspect of inventory and supply chain management. Due to the sporadic nature of the demand, demand forecasting involves dealing with intermittent time series in domains such as retail, manufacturing. Conventional forecasting methods do not work well for intermittent time series due to inherent sparsity in such series. Researchers have proposed multiple methods to deal with intermittent time series such as Croston and its variants. Our work aims to provide an insight into the various forecasting methods traditionally known to work well for forecasting intermittent series. We have also explored deep learning methods that have been proposed in recent literature. These methods are thoroughly reviewed and explained in this survey paper. Additionally, experiments are done on two publicly available datasets to compare the performance of the traditional methods with deep learning models. Furthermore, a hybrid model made of independent classification and regression trees has been implemented and studied as well. We provide a comprehensive evaluation that aims at selecting the appropriate method, given the dataset, context, and objectives that have to be met by the forecasting practitioner/researcher.