Judgmental forecasts of time series affected by special events: does providing a statistical forecast improve accuracy?

Time series found in areas such as marketing and sales often have regular established patterns which are occasionally affected by exogenous influences, such as sales promotions. While statistical forecasting methods are adept at extrapolating regular patterns in series, judgmental forecasters have a potential advantage in that they can take into account the effect of these external influences, which may occur too infrequently for reliable statistical estimation. This suggests that a combination of statistical method and judgment is appropriate. An experiment was conducted to examine how judgmental forecasters make use of statistical time series forecasts when series are subject to sporadic special events. This was investigated under different conditions which were created by varying the complexity of the time series signal, the level of noise in the series, the salience of the cue, the predictive power of the cue information and the availability and presentation of the statistical forecast. Although the availability of a statistical forecast improved judgment under some conditions, the use the judgmental forecasters made of these forecasts was far from optimal. They changed the statistical forecasts when they were highly reliable and ignored them when they would have formed an ideal base-line for adjustment.