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Papers Related to RBF

  • Monica Adya, Fred Collopy, J. Scott Armstrong, and Miles Kennedy (2001), "Automatic Identification of Time Series Features for Rule-Based Forecasting", International Journal of Forecasting, 17, 143-157. Full Text (LINK TO: ) - Automatic procedures, which areless expensive and more reliable than judgmental procedures, produced rule-based forecasts with little loss in forecast accuracy.
  • Adya, M., 2000 "Corrections to Rule-based Forecasting: Findings from a Replication", International Journal of Forecasting, 16, 1, 125-128. Full Text
  • Adya, M., J. Armstrong, F. Collopy, & M. Kennedy (2000), "An Application of Rule-based Forecasting for a Situation Lacking Domain Knowledge," International Journal of Forecasting, 16, 477-484. Full Text – This paper was part of the M3-competition. A simplified version of RBF performed well without using domain knowledge.
  • Balhadjali, M., M. Luska, & D. Matzner, (2004), “A Test of a Minimalist Rule-Based Forecasting System,” Working paper series - retrieved March 13, 2008, from http://www.ww.uni-magdeburg.de/bwl2/.- Full Text
  • Fred Collopy and J. Scott Armstrong (1992), "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, 38 (10), 1394-1414. - Full Text – Uses prior knowledge about forecasting methods and domain knowledge to formulate rules for time series forecasting.
  • Fred Collopy and J. Scott Armstrong (1989), "Toward Computer-Aided Forecasting Systems: Gathering, Coding, and Validating the Knowledge," in George R. Widmeyer (ed.), DSS-899 Transactions: Ninth International Conference on Decision Support Systems, Institute of Management Science, pp. 103-119 – Describes how to develop rules for forecasting.
  • Gardner, Jr., E.S. (1999), "Rule-Based Forecasting Vs. Damped-Trend Exponential Smoothing," Management Science, 45, (8, August), 1169-1176.
  • Makridakis, S., & Hibon, M. (2000), "The M3-Competition: Results, Conclusions, and Impolications," International Journal of Forecasting, 16, 451-476. - Full Text

Papers Related to Causal Forces

  • J. S. Armstrong, Fred Collopy, and J. Thomas Yokum, "Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series," forthcoming in International Journal of Forecasting. – Full Text
  • J.Scott Armstrong and Fred Collopy (2001), "Identification of Asymmetric Prediction Intervals through Causal Forces," Journal of Forecasting, 20, 273-283. Full Text – When forecast errors are large, as is common in annual forecasting, errors are asymmetrical in percentage terms. Log transformations can correct for this asymmetry, although asymmetry in the logs occurs for "contrary" time series.
  • J.Scott Armstrong and Fred Collopy (1993), "Causal Forces: Structuring Knowledge for Time-series Extrapolation," Journal of Forecasting, 12, 103-115. Full Text – Domain knowledge, expressed as expectations about trends in time series, led to improved accuracy in time-series forecasts.

Studies that use Production Rules for Forecasting

  • Li, X., Ang, C.L., & Gray, R. (1999), "An Intelligent Business Forecaster for Strategic Business Planning," Journal of Forecasting, 18(3), 181-204.
  • Miller, P.L. Frawley, S.J., Sayward, F.G., Yasnoff, W.A., Duncan, L., and Fleming, D.W. 1977), "Combining Tabular, Rule-based, and Procedural Knowledge in Compter-Based Guidelines for Childhood Immunization," Computers and Biomedical Resarch, 30, 211-231. Full Text
  • Nikolopoulos, N. & Assimakopoulos, V. (2003), "Theta Intelligent Forecasting Information System," Industrial Management & Data Systems, 103(9), 711-726
  • Rahman, S. (1990), "Formulation and Analysis of a rule-based short-term load forecasting algorithm," Proceedings of the IEEE, 78(5), 805-816.
  • Rahman, S. & Baba, M. (1989), "Software Design and Evaluation of a Microcomputer-based Automated Forecasting System," IEEE Transaction on Power Systems, 4(2), 782-788.
  • Tavanidou, E., Nikolopoulos, K., Metaxiotis, K., and Assimakopoulos, V. (2003), "An Innovative e-Forecasting Web Application," International Journal of Software Engineering and Knowledge Engineering, 13(2), 215-236.

Studies that use Data and Time Series Features to Select Forecasting

  • Prudencio, R. & Ludermir, T. (2004), "Using Machine Learning Techniques to Combine Forecasting Methods," in AI2004: Advances in Artificial Intelligence, 3339/2004, 1122-1127.
  • Prudencio, R., Ludermir, T., & de Carvalho, F. (2004), "A Model Symbolic Classifier for Selecting Time Series Models," Pattern Recognition Letters, 25(8), 911-921.
  • Venkatachalam, A.R. & Stohl, J.E. (1999), "An Intelligent Model Selection and Forecasting System, " Journal of Forecasting, 18(3), 167-180.

Other Relevant Research on RBF

  • Armstrong, J. (2006), "Findings from Evidence-based Forecasting: Methods for Reducing Forecast Error, " International Journal of Forecasting, 22, 583-598. - Full Text
  • Bunn, D., L. Menezes, & J. Taylor (2000), "Review of Guidelines for the Use of Combined Forecasts," European Journal of Operational Research, 120, 190-204. - Full Text
  • Webb, G. (2003), "A Rule Based Forecast of Computer Hard Drive Costs,"Issues in Information Systems, 4, 337-343. Full Text
  • Cohen, E., & Z. Schwartz, (2004), "Hotel Revenue-Management Forecasting: Evidence of Expert-Judgment Bias," Cornell Hotel & Restaurant Administration Quarterly, February.- Full Text
  • Shah, C. (1997), "Model Selection in Univariate Time Series Forecasting Using Discriminant Analysis, " International Journal of Forecasting, 13 (4), 489-500.
  • Morwitz, V.G. and D.C. Schmittlein, (1998) "Testing New Direct Marketing Offerings: The Interplay of Management Judgment and Statistical Models", Management Science, 44 (5, May), 610-628.
  • Tashman, L. "Out-of-sample tests of forecasting accuracy: An analysis and review", International Journal of Forecasting, 2000, Vol. 16(4), 437-450.
  • Vokurka, R.J. and B.E. Flores, S.L. Pearce (1996), "Automatic feature identification and graphical support in Rule-based forecasting: A comparison", International Journal of Forecasting, 12, 495-512.
  • Armstrong, J.S. (1999), "Forecasting for Environmental Decision Making," in Dale, V.H. & English, M.E. (eds.), Tools to Aid Environmental Decision Making, New York: Springer-Verlag, pp 192-225. - Full Text
  • Armstrong, J.S. (1989), "Combining Forecasts: The End of the Beginning or the Beginning of the End," International Journal of Forecasting, 5, 585-599. - Full Text
  • Armstrong, J.S. & Brodie, R.J. (1999), "Forecasting for marketing," in Hooley, G.J. & Hussey, M.K. (eds), Quantitative Methods in Marketing, 2nd Ed., London: International Thompson Business Press, pp. 92-119. - Full Text
  • De Menezes, L., Bunn, D.W., & Taylor, J.W. (2000), "Review of Guidelines for the Use of Combined Forecasts," European Journal of Operational Research, 120, 190-224. - Full Text
  • Sanders, N. & Ritzman, L.P. (2004) "Integrating Judgmental and Quantitative Forecasts: Methdologies for Pooling Marketing and Operations Information," International Journal of Operations & Production Management, 24(5), 514-529. - Full Text
  • Tashman, L.J. & Kruk, J.M. (1996), "The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions," International Journal of Forecasting , 12(2), 235-253.

Global Health and RBF

  • Green, K. C. & J. S. Armstrong (2007), “Global Warming: Forecasts by Scientists Versus Scientific Forecasts,” Energy and Environment, 18, 997-1021. Full Text
  • Sekhri, N., (2006), “Forecasting for Global Health: New Money, New Products & New Markets,” Retrieved March 13, 2008, from http://www.cgdev.org/doc/ghprn/. - Full Text

Demography and RBF

  • Bijak, J., (2006), “Forecasting International Migration: Selected Theories, Models and Methods,” Retrieved March 13, 2008, from http://www.cefmr.pan.pl/. Full Text

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