LAUSR

There are several biases and inefficiencies that are commonly associated with the judgmental extrapolation of time series, even when the forecasters have technical knowledge about forecasting. This study examines the effectiveness of using a rolling training approach, based on feedback, to improve the accuracy of forecasts elicited from people with such knowledge. In an experiment, forecasters were asked to make multiple judgmental extrapolations for a set of time series from different time origins. For each series in turn, the participants were either unaided or provided with feedback. In the latter case, the true outcomes and performance feedback were provided following the submission of each set of forecasts. The objective was to provide a training scheme that would enable forecasters to understand the underlying pattern of the data better by learning from their forecast errors directly. An analysis of the results indicated that this rolling training approach is an effective method for enhancing the judgmental extrapolations elicited from people with technical knowledge, especially when bias feedback is provided. As such, it could be a valuable element in the design of software systems that are intended to support expert knowledge elicitation (EKE) in forecasting.