Forecasting interrupted time series

Forecasting interrupted time series data is a major challenge for forecasting teams, especially in light events such as the COVID-19 pandemic. This paper investigates several strategies for dealing with in time series forecasting, including highly adaptable models, intervention models, marking interrupted periods as missing, forecasting what may have been, downweighting the interruption, and ensemble models. Each approach offers specific advantages and disadvantages, such as, memory retention, data integrity, flexibility, and accuracy. We evaluate the effectiveness these strategies using two actual datasets that were interrupted by COVID-19, and we provide for how to handle these interruptions. This work contributes to the literature on series forecasting, offering insights for academics and practitioners dealing with interrupted in numerous domains.