Forecasting interrupted time series
研究了多种处理中断时间序列预测的策略，包括高适应性模型、干预模型等，并用COVID-19中断的实际数据集评估效果，为预测团队提供建议。
Forecasting interrupted time series data is a major challenge for forecasting teams, especially in light of events such as the COVID-19 pandemic. This paper investigates several strategies for dealing with interruptions in time series forecasting, including highly adaptable models, intervention models, marking interrupted periods as missing, forecasting what may have been, downweighting the interruption period, and ensemble models. Each approach offers specific advantages and disadvantages, such as adaptability, memory retention, data integrity, flexibility, and accuracy. We evaluate the effectiveness of these strategies using two actual datasets that were interrupted by COVID-19, and we provide recommendations for how to handle these interruptions. This work contributes to the literature on time series forecasting, offering insights for academics and practitioners dealing with interrupted data in numerous domains.