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

Adya, 2000, Corrections to rule-based forecasting: Findings from a replication, International Journal of Forecasting, 16, 125, 10.1016/S0169-2070(99)00034-5 Adya, 2000, An application of rule-based forecasting to a situation lacking domain knowledge, International Journal of Forecasting, 16, 477, 10.1016/S0169-2070(00)00074-1 Adya, 1998, How effective are neural networks at forecasting and prediction? A review and evaluation, Journal of Forecasting, 17, 481, 10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.0.CO;2-Q Adya, 2001, Automatic identification of time series features for rule-based forecasting, International Journal of Forecasting, 17, 143, 10.1016/S0169-2070(01)00079-6 Allen, 2001, Econometric forecasting Armstrong, 1985 Armstrong, 2001 Armstrong, 2001, Judgmental bootstrapping, 169 Armstrong, 1993, Causal forces: Structuring knowledge for time-series extrapolation, Journal of Forecasting, 12, 103, 10.1002/for.3980120205 Armstrong, 1998, Integration of statistical methods and judgment for time-series forecasting: Principles from empirical research, 69 Armstrong, 2005, Decomposition by causal forces: A procedure for forecasting complex time series, International Journal of Forecasting, 21, 25, 10.1016/j.ijforecast.2004.05.001 Armstrong, 2003, Reaping benefits from management research: Lessons from the forecasting principles project, Interfaces, 33, 89, 10.1287/inte.33.6.91.25180 Avorn, 2004 Batchelor, 1995, Forecaster diversity and the benefits of combining forecasts, Management Science, 41, 68, 10.1287/mnsc.41.1.68 Bunn, 1999, Comparison of seasonal estimation methods in multi-item short-term forecasting, International Journal of Forecasting, 15, 431, 10.1016/S0169-2070(99)00005-9 Chatfield, 1993, Neural networks: Forecasting breakthrough or passing fad?, International Journal of Forecasting, 9, 1, 10.1016/0169-2070(93)90043-M Collopy, 1992, Rule-based forecasting: Development and validation of an expert systems approach to combining time series extrapolations, Management Science, 38, 1394, 10.1287/mnsc.38.10.1394 Dana, 2004, The superiority of simple alternatives to regression for social science predictions, Journal of Educational and Behavioral Statistics, 29, 317, 10.3102/10769986029003317 Dangerfield, 1992, Top-down or bottom-up: Aggregate versus disaggregate extrapolations, International Journal of Forecasting, 8, 233, 10.1016/0169-2070(92)90121-O Fildes, 2006, The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion, International Journal of Forecasting, 22, 415, 10.1016/j.ijforecast.2006.03.002 Fildes, 1998, Generalizing about univariate forecasting methods: Further empirical evidence, International Journal of Forecasting, 14, 339, 10.1016/S0169-2070(98)00009-0 Ganzach, 2000, Making decisions from an interview: Expert measurement and mechanical combination, Personnel Psychology, 53, 1, 10.1111/j.1744-6570.2000.tb00191.x Gardner, 1985, Exponential smoothing: The state of the art, Journal of Forecasting, 4, 1, 10.1002/for.3980040103 Gardner, 1990, Evaluating forecast performance in an inventory control system, Management Science, 36, 490, 10.1287/mnsc.36.4.490 Gardner, 1993, Forecasting the failure of component parts in computer systems: A case study, International Journal of Forecasting, 9, 245, 10.1016/0169-2070(93)90008-B Gardner, 1997, Focus forecasting reconsidered, International Journal of Forecasting, 13, 501, 10.1016/S0169-2070(97)00035-6 Gardner, 1985, Forecasting trends in time series, Management Science, 31, 1237, 10.1287/mnsc.31.10.1237 Goodwin, 2005, How to integrate judgment with statistical forecasts, Foresight: The International Journal of Applied Forecasting, 1, 8 Gorr, 2003, Short-term forecasting of crime, International Journal of Forecasting, 19, 579, 10.1016/S0169-2070(03)00092-X Green, 2002, Forecasting decisions in conflict situations: A comparison of game theory, role-playing, and unaided judgement, International Journal of Forecasting, 18, 321, 10.1016/S0169-2070(02)00025-0 Green, 2005, Game theory, simulated interaction, and unaided judgement for forecasting decisions in conflicts: Further evidence, International Journal of Forecasting, 21, 463, 10.1016/j.ijforecast.2005.02.006 Green, K.C. & Armstrong, J.S. (2006). Structured analogies for forecasting. Department of Marketing Working Paper, The Wharton School. Grove, 2000, Clinical versus mechanical prediction: A meta-analysis, Psychological Assessment, 12, 19, 10.1037/1040-3590.12.1.19 Jørgensen, 2004, Top–down and bottom–up expert estimation of software development effort, Journal of Information and Software Technology, 46, 3, 10.1016/S0950-5849(03)00093-4 Jørgensen, 2004, A review of studies on expert estimation of software development effort, Journal of Systems and Software, 70, 37, 10.1016/S0164-1212(02)00156-5 Keogh, 2002, On the need for time series data mining benchmarks: A survey and empirical demonstration, 102 Lewis, 2003 Liao, 2005, The accuracy of a procedural approach to specifying feed forward neural networks for forecasting, Computers & Operations Research, 32, 2151, 10.1016/j.cor.2004.02.006 Lichtman, 2005, The keys to the White House: Forecast for 2008, Foresight: The International Journal of Applied Forecasting, 3, 5 MacGregor, 2001, Decomposition in judgmental forecasting and estimation Makridakis, 1982, The accuracy of extrapolation (time series) methods: Results of a forecasting competition, Journal of Forecasting, 1, 111, 10.1002/for.3980010202 Makridakis, 1993, The M2-Competition: A real-time judgmentally based forecasting study (with commentary), International Journal of Forecasting, 9, 5, 10.1016/0169-2070(93)90044-N Makridakis, 2000, The M3-Competition: Results, conclusions and implications, International Journal of Forecasting, 16, 451, 10.1016/S0169-2070(00)00057-1 Meade, 2001, Forecasting the diffusion of innovations: Implications for time series extrapolation Meehl, 1956, Wanted: A good cookbook, American Psychologist, 11, 263, 10.1037/h0044164 Miller, 2003, Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy, International Journal of Forecasting, 19, 669, 10.1016/S0169-2070(02)00077-8 Miller, 2004, Shrinkage estimators for damping X12-ARIMA seasonals, International Journal of Forecasting, 20, 529, 10.1016/j.ijforecast.2004.03.002 Miller, 1993, Seasonal exponential smoothing with damped trends: An application for production planning, International Journal of Forecasting, 9, 509, 10.1016/0169-2070(93)90077-Z Rowe, 2001, Expert opinions in forecasting: The role of the Delphi technique, 125 Schnaars, 1986, A comparison of extrapolation models on yearly sales forecasts, International Journal of Forecasting, 2, 71, 10.1016/0169-2070(86)90031-2 Surowiecki, 2004 Vokurka, 1996, Automatic feature identification and graphical support in rule-based forecasting: A comparison, International Journal of Forecasting, 12, 495, 10.1016/S0169-2070(96)00682-6 Wittink, 2001, Forecasting with conjoint analysis, 147 Wolfers, 2004, Prediction markets, Journal of Economic Perspectives, 18, 107, 10.1257/0895330041371321 Wong, 2000, A bibliography of neural network business applications research: 1994–1998, Computers & Operations Research, 27, 1045, 10.1016/S0305-0548(99)00142-2