Judgemental forecasting in times of change

This paper reports a study which examines the ability of people and statistical models to forecast time series which contain major discontinuities. It has often been suggested that human judgement will be superior when circumstances change dramatically and statistical models are no longer relevant. Using ten time series that contained five different discontinuities and two levels of randomness, the results indicated that people performed significantly worse than (parsimonious) statistical models. This occurred for the segments of the time series where the discontinuity was to be found and for the subsequent segment where the series was stable. People seemed to change their forecasts in response to random fluctuations in the time series, identifying a signal where it did not exist. This was especially true for the series with high variability. The implications of these results for forecasting practices are discussed.