Accuracy of judgmental extrapolation of time series data: Characteristics, causes, and remediation strategies for forecasting

This paper links social judgment theory to judgmental forecasting of time series data. Individuals were asked to make forecasts for 18 different time series that were varied systematically on four cues: long-term levels, long-term trends, short-term levels, and the magnitude of the last data point. A model of each individual's judgment policy was constructed to reflect the extent to which each cue influenced the forecasts that were made. Participants were assigned to experimental conditions that varied both the amount of information and the forecasting horizon; ‘special events’ (i.e. discontinuities in the time series) also were introduced. Knowledge and consistency were used as measures of the judgment process, and MPE and MAPE were used as measures of forecast performance. Results suggest that consistency is necessary but not sufficient for the successful application of judgment to forecasting time series data. Information provided for forecasters should make long-term trends explicit, while the task should be limited to more immediate forecasts of one or two steps ahead to reduce recency bias. This paper provides one method of quantifying the contributions and limitations of judgment in forecasting.