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Volume 22 Issue 3 (July-September 2006)

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Twenty five years of forecasting
edited by Rob J Hyndman, J. Keith Ord

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong, J.S.
Pages 583-598
Abstract

Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses tests should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory.

Keywords: Box-Jenkins , Causal forces , Causal models , Combining forecasts , Complex series , Conjoint analysis , Contrary series , Damped seasonality , Damped trend , Data mining , Delphi , Diffusion , Game theory , Judgmental decomposition , Multiple hypotheses , Neural nets , Prediction markets , Rule-based forecasting , Segmentation , Simulated interaction , Structured analogies
FULL TEXT LINK
http://dx.doi.org/10.1016/j.ijforecast.2006.04.006
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