Intermittent Demand Forecasting with Deep Renewal Processes [article]

Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
2019 arXiv   pre-print
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between renewal processes, and a collection of current models used for intermittent demand forecasting. We then develop a set of models that benefit from recurrent neural networks to parameterize conditional interdemand time and size distributions, building on the latest paradigm in "deep" temporal point processes. We present
more » ... favorable empirical findings on discrete and continuous time intermittent demand data, validating the practical value of our approach.
arXiv:1911.10416v1 fatcat:j6u5fhghrjdine22qycigbvtyq