Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes.

ABSTRACT: Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but additionally for a natural inclusion of neural network based models-by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling con

SUBMITTER: Turkmen AC 

PROVIDER: S-EPMC8629246 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature