Forecasting time series perturbed by external events is a difficult challenge both for statistical models and for forecasters using their judgment. External events can disturb the historical timeline significantly and add complexity. But not all external events are the same. Here, we first provide a taxonomy of external events in the context of forecasting from time series by classifying both the properties of the events themselves and the characteristics of their impacts. We then discuss research into the various ways in which judgment is used in making forecasts from time series disrupted by external events. The evidence suggests that it is generally flawed and susceptible to inconsistencies and various biases. However, there may be ways in which these problems can be minimized. We go on to discuss developments in the world of modelling and statistical forecasting in this area. There are now analytical techniques that enable disturbances caused by external events to be incorporated into time series forecasting. Some of these models are transparent: the features of the data that they extract and the way in which they are processed is made explicit. Other models, particularly those using machine learning techniques, are not transparent: the manner in which they process the data is hidden within a ‘black box’. As yet, it is not clear which of these two approaches produces more accurate forecasts. However, we suggest that transparent approaches are likely to be more acceptable to both forecasters and users of forecasts.
|Title of host publication||Judgment in Predictive Analytics |
|Number of pages||23|
|ISBN (Print)||978-3-031-30084-4, 978-3-031-30087-5|
|Publication status||Published - 3 Jun 2023|
|Series||International Series in Operations Research and Management Science|