Forecasting for disrupted demand: evidence from tourism arrivals

This study examines various forecasting strategies in the presence of a disruption, using Hong Kong tourism demand as a case study. The following strategies consist of (1) developing adaptive models, (2) incorporating a disruption into an adaptive model, (3) interpolating the interrupted period of the demand, and (4) controlling the size of the training sets. The bias-variance trade-off provides a theoretical understanding of when a strategy produces more accurate forecasts than other strategies. The study employs single-source-of-error state-space models, namely exponential smoothing and autoregressive integrated moving average, to produce forecasts. This study also assesses their performance relative to the seasonal-trend model. The findings show that, regardless of the strategies, using all observations is preferable to using post-disruption observations in terms of forecast accuracy. Using mixed-effects regression analysis, interpolating the interrupted demand that maintains the structures of the demand improves the forecast accuracy the most, even though the interpolating may introduce propagated errors. If a modeller decides to use only post-disruption data, they need to consider a trade-off between assuming the pre-disruption model structure the same as the post one or implementing automatic model selection with backcasting estimation.