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Full-Text Articles in Physical Sciences and Mathematics
Causal Forces: Structuring Knowledge For Time Series Extrapolation, J. Scott Armstrong, Fred Collopy
Causal Forces: Structuring Knowledge For Time Series Extrapolation, J. Scott Armstrong, Fred Collopy
J. Scott Armstrong
This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determination of weights for combining extrapolation forecasts. These weights improved average ex ante forecast accuracy when tested on 104 annual economic and demographic time series. Gains in accuracy were greatest when (1) the causal forces were clearly specified and (2) stronger causal …
Findings From Evidence-Based Forecasting: Methods For Reducing Forecast Error, J. Scott Armstrong
Findings From Evidence-Based Forecasting: Methods For Reducing Forecast Error, J. Scott Armstrong
J. Scott Armstrong
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 …