Forecasting Intermittent Demand Patterns with Time Series and Machine Learning Methodologies

The study aims to generalize the predictive accuracy of various machine learning approaches, along with the widely used Croston’s method for time-series forecasting. Using multiple multi-period time-series we see if there is... [ view full abstract ]

The study aims to generalize the predictive accuracy of various machine learning approaches, along with the widely used Croston’s method for time-series forecasting. Using multiple multi-period time-series we see if there is a method that tends to capture intermittent demand better than others. The motivation of this study is that demand forecasting is an important component of business planning, but it is also a challenge, especially in the case for intermittent demand. If companies cannot capture the demand accurately, companies either risk losing sales and customers when items are stocked out, or are burdened with excessive inventories. Therefore, being able to identify even small improvements in prediction accuracy of intermittent demand can translate into significant savings (Aris A. Syntetos, Zied Babai, & Gardner, 2015). In collaboration with a supply chain consulting company, we investigated over 160 different intermittent time-series to identify what works the best.