Forecasting Seasonal Changepoint Models Utilizing Changepoint Uncertainties

Music City Center 

Forecasting in the presence of changepoints is a challenging task. If changepoints occur regularly within seasonal data, we should utilize that information in our predictions for the next season. In this paper we develop a framework for forecasting seasonal data that contains within-season changepoints. In our forecasts, we account for uncertainty in the changepoint locations both within and across seasons via a weighting approach across changepoint uncertainties. The framework is very flexible across different changepoint model assumptions and approaches to identifying confidence sets. We demonstrate the improvement in forecasting performance of our framework for two business data applications.