Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge

Petropoulos, Fotios and Goodwin, Paul and Fildes, Robert (2017) Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge. International Journal of Forecasting, 33 (1). pp. 314-324. ISSN 0169-2070

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Abstract

Several biases and inefficiencies are commonly associated with the judgmental extrapolation of time series even when 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 they were provided with feedback. In the latter case, following submission of each set of forecasts, the true outcomes and performance feedback were provided. The objective was to provide a training scheme, enabling forecasters to better understand the underlying pattern of the data by learning directly from their forecast errors. Analysis of the results indicated that the rolling training approach is an effective method for enhancing judgmental extrapolations elicited from people with technical knowledge, especially when bias feedback is provided. As such it can be a valuable element in the design of software systems that are intended to support expert knowledge elicitation (EKE) in forecasting.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Forecasting
Additional Information:
© 2016 The Authors. Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1403
Subjects:
ID Code:
78764
Deposited By:
Deposited On:
18 Mar 2016 14:40
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Dec 2020 03:21