Findings from Evidence-Based Forecasting: Methods for Reducing Forecast Error

By Scott Armstrong. 2006

215 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

The Tourism Forecasting Competition (2011, background): “http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/”
A comparison of threats, vulnerabilities and management approaches in global seagrass bioregions (2012, background): “An additional 10 individuals with expertise in bioregions that were not adequately represented at the workshop were also invited to participate in the survey to ensure representation of the whole expert community (Armstrong 2006).”
Golden Rule of Forecasting: Be conservative (2015, unknown): “Experimental evidence demonstrates that it is easy to find 18 structured combining methods that produce forecasts from expert judgments that are more accurate than those from traditional group meetings (Armstrong, 2006b).”
Damped trend exponential smoothing: A modelling viewpoint (2010, unknown): “In a review of evidence-based forecasting, Armstrong (2006) recommended the damped trend as a well established forecasting method that”
Significance Tests Harm Progress in Forecasting (2007, unknown): “More recently (Armstrong 2006), I reviewed evidence on which methods have been shown to improve the accuracy of forecasts over the past quarter century.”
Forecasting in social settings: The state of the art (2020, methodology): “Another pioneering paper was published twenty years later by Armstrong (2006), aimed at “summarizing what has been learned over the past quarter century about the accuracy of forecasting methods” (p.583) while covering new developments, including neural networks that were in their infancy at that…”
An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction (2013, methodology): “Because traffic flow is uncertain, nonlinear and complex, it is difficult to predict the traffic flow effectively and accurately by the predict method based on traditional mathematics and physics models (Armstrong, 2006).”
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011, result): “As a consequence, Chatfield (as quoted by Armstrong, 2006) suspects a positive bias in NN publications, due to a " file-drawer problem " of negative results, leading Armstrong (2006) to conclude that too much research effort is being devoted to this method.”
An Introductory Study on Time Series Modeling and Forecasting (2013, unknown): “A lot of works have been done towards this direction and various combining methods have been proposed in literature [8, 14, 15, 16].”