Part of the International Series in Operations Research & Management Science book series [ISOR,volume 30]
Six ways of selecting forecasting methods are described: Convenience, “what’s easy,” is inexpensive but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods.Abstract
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Adya, M., J. S. Armstrong, F. Collopy and M. Kennedy [2000], “An application of rule-based forecasting to a situation lacking domain knowledge,” International Journal of Forecasting, 16, 477–484. CrossRef Google Scholar Ahlburg, D. [1991], “Predicting the job performance of managers: What do the experts know?” International Journal of Forecasting, 7, 467–472. CrossRef Google Scholar Allen, P. G. and R. Fildes [2001], “Econometric forecasting,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers. Google Scholar Armstrong, J. S. [1984], “Forecasting by extrapolation: Conclusions from twenty-five years of research,” [with commentary], Interfaces, 14 [Nov.—Dec.], 52–66. Full text at hops.wharton.upenn.edu/forecast. CrossRef Google Scholar Armstrong, J. S. [1985], Long-Range Forecasting: From Crystal Ball to Computer [2“d ed.]. New York: John Wiley. Full text at hops.wharton.upenn.edu/forecast Google Scholar Armstrong, J. S. [2001a], “Evaluating forecasting methods,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers. Google Scholar Armstrong, J. S. [2001b], “Judgmental bootstrapping: Inferring experts’ rules for forecasting,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers. Google Scholar Armstrong, J. S. [2001c], “Combining forecasts,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA. Kluwer Academic Publishers. Google Scholar Armstrong, J. S. [2001d], “Standards and practices for forecasting,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers. Google Scholar Armstrong, J. S. [2001e], “Role Playing: A Method to Forecast Decisions,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers. Google Scholar Armstrong, J. S., M. Adya and F. Collopy [2001], “Rule-based forecasting: Using judgment in time-series extrapolation,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA. Kluwer Academic Publishers.References
Google Scholar
Armstrong, J. S., R. Brodie and S. McIntyre [1987], “Forecasting methods for marketing,” International Journal of Forecasting, 3, 355–376. Full text at hops.wharton.upenn.edu/forecast
CrossRef Google Scholar
Armstrong, J. S. and M. Grohman [1972], “A comparative study of methods for long-range market forecasting,” Management Science, 19, 211–221. Full text at hops.wharton.upenn.edu/forecast
CrossRef Google Scholar
Armstrong, J. S. and T. Overton [1971], “Brief vs. comprehensive descriptions in measuring intentions to purchase,” Journal of Marketing Research, 8, 114–117. Full text at hops.wharton.upenn.edu/forecast.
CrossRef Google Scholar
Assmus, G., J. U. Farley and D. R. Lehmann [1984], “How advertising affects sales: A meta-analysis of econometric results,” Journal of Marketing Research, 21, 65–74.
CrossRef Google Scholar
Bailey, C. D. and S. Gupta [1999], “Judgment in learning-curve forecasting: A laboratory study,” Journal of Forecasting, 18, 39–57.
CrossRef Google Scholar
Braun, P. A. and I. Yaniv [1992], “A case study of expert judgment: Economists’ probabilities versus base-rate model forecasts,” Journal of Behavioral Decision Making, 5, 217–231.
CrossRef Google Scholar
Bretschneider, S. I., W. L. Gorr, G. Grizzle and E. Klay [1989], “Political and organizational influences on the accuracy of forecasting state government revenues,” International Journal of Forecasting, 5, 307–319.
CrossRef Google Scholar
Buehler, R., D. Griffin and M. Ross [1994], “Exploring the `planning fallacy’: Why people underestimate their task completion times,” Journal of Personality and Social Psychology, 67, 366–381.
CrossRef Google Scholar
Campion, M. A., D. K. Palmer and J. E. Campion [1997], “A review of structure in the selection interview,” Personnel Psychology, 50, 655–701.
CrossRef Google Scholar
Carbone, R. and J. S. Armstrong [1982], “Evaluation of extrapolation forecasting methods: Results of a survey of academicians and practitioners,” Journal of Forecasting, 1, 215–217. Full text at hops.wharton.upenn.edu/forecast
CrossRef Google Scholar
Chambers, J. C., S. Mullick and D. D. Smith [1971], “How to choose the right forecasting technique,” Harvard Business Review, 49, 45–71.
Google Scholar
Chambers, J. C., S. Mullick and D. D. Smith [1974], An Executive ‘s Guide to Forecasting. New York: John Wiley.
Google Scholar
Collopy, F., M. Adya and J. S. Armstrong [2001], “Expert systems for forecasting,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA. Kluwer Academic Publishers.
Google Scholar
Cooper, A., C. Woo and W. Dunkelberg [1988], “Entrepreneurs’ perceived chances for success,” Journal of Business Venturing, 3, 97–108.
CrossRef Google Scholar
Cowles, A. [1933], “Can stock market forecasters forecast?” Econometrica, 1, 309–324.
CrossRef Google Scholar
Cox, J. E. Jr. and D. G. Loomis [2001], “Diffusion of forecasting principles: An assessment of books relevant to forecasting,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Google Scholar
Dahan, E. and V. Srinivasan [2000], “The predictive power of internet-based product concept testing using visual depiction and animation,” Journal of Product Innovation Management, 17, 99–109.
CrossRef Google Scholar
Dakin, S. and J. S. Armstrong [1989], “Predicting job performance: A comparison of expert opinion and research findings,” International Journal of Forecasting, 5, 187–194. Full text at hops.wharton.upenn.edu/forecast.
CrossRef Google Scholar
Dalrymple, D. J. [1987], “Sales forecasting practices: Results from a United States survey,” International Journal of Forecasting, 3, 379–391.
CrossRef Google Scholar
Fildes, R. [1989], “Evaluation of aggregate and individual forecast method selection rules, Management Science, 35, 1056–1065.
CrossRef Google Scholar
Frank, H. A. and J. McCollough [1992] “Municipal forecasting practice: `Demand’ and `supply’ side perspectives,” International Journal of Public Administration, 15, 1669–1696.
CrossRef Google Scholar
Freyd, M. [1925], “The statistical viewpoint in vocational selection,” Journal of Applied Psychology, 9, 349–356.
CrossRef Google Scholar
Fullerton, D. and T. C. Kinnaman [1996], “Household responses to pricing garbage by the bag,” American Economic Review, 86, 971–984.
Google Scholar
Georgoff, D. M. and R. G. Murdick [1986], “Manager’s guide to forecasting,” Harvard Business Review, 64, January-February, 110–120.
Google Scholar
Grove, W. M. and P. E. Meehl [1996], “Comparative efficiency of informal [subjective, impressionistic] and formal [mechanical, algorithmic] prediction procedures: The clinical-statistical controversy,” Psychology, Public Policy and Law, 2, 293–323.
CrossRef Google Scholar
Kahneman, D. and D. Lovallo [1993], “Timid choices and bold forecasts: A cognitive perspective on risk taking,” Management Science, 39, 17–31.
CrossRef Google Scholar
Lemert, J. B. [1986], “Picking the winners: Politician vs. voter predictions of two controversial ballot measures,” Public Opinion Quarterly, 50, 208–221.
CrossRef Google Scholar
Lewis-Beck, M. S. and C. Tien [1999], “Voters as forecasters: A micromodel of election prediction,” International Journal of Forecasting, 15, 175–184.
CrossRef Google Scholar
Locke, E. A. [1986], Generalizing from Laboratory to Field Settings. Lexington, MA: Lexington Books.
Google Scholar
MacGregor, D. G. [2001], “Decomposition for judgmental forecasting an estimation,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Google Scholar
Mahmoud, E., G. Rice and N. Malhotra [1986], “Emerging issues in sales forecasting on decision support systems,” Journal of the Academy of Marketing Science, 16, 47–61.
CrossRef Google Scholar
Makridakis, S. [1990], “Sliding simulation: A new approach to time series forecasting,” Management Science, 36, 505–512.
CrossRef Google Scholar
Makridakis, S., A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen and R. Winkler [1982], “The accuracy of extrapolation [time series] methods: Results of a forecasting competition,” Journal of Forecasting, 1, 111–153.
CrossRef Google Scholar
Meehl, P. E. [1954], Clinical vs. Statistical Prediction. MN: University of Minnesota Press.
Google Scholar
Mentzer, J. T. and J. E. Cox, Jr. [1984], “Familiarity, application, and performance of sales forecasting techniques,” Journal of Forecasting, 3, 27–36
CrossRef Google Scholar
Mentzer, J. T. and K. B. Kahn [1995], “Forecasting technique familiarity, satisfaction, usage, and application,” Journal of Forecasting, 14, 465–476.
CrossRef Google Scholar
Rhyne, D. M. [1989], “Forecasting systems in managing hospital services demand: A review of utility,” Socio-economic Planning Sciences, 23, 115–123.
CrossRef Google Scholar
Rowe, G. and G. Wright [2001], “Expert opinions in forecasting: Role of the Delphi technique,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Google Scholar
Sanders, N. R. and K. B. Manrodt [1994], “Forecasting practices in U. S. corporations: Survey results,” Interfaces, 24 [2], 92–100.
CrossRef Google Scholar
Sarbin, T. R. [1943], “A contribution to the study of actuarial and individual methods of prediction,” American Journal of Sociology, 48, 593–602.
CrossRef Google Scholar
Schnaars, S. P. [1984], “ Situational factors affecting forecast accuracy,” Journal ofMarketing Research, 21, 290–297.
CrossRef Google Scholar
Sethuraman, R. and G. J. Tellis [1991], “An analysis of the tradeoff between advertising and price discounting,” Journal of Marketing Research, 28, 160–174.
CrossRef Google Scholar
Sherden, W. A. [1998], The Fortune Sellers. New York: John Wiley.
Google Scholar
Slovic, P. and D. J. McPhillamy [1974], “Dimensional commensurability and cue utilization in comparative judgment,” Organizational Behavior and Human Performance, 11, 172–194.
CrossRef Google Scholar
Smith, S. K. [1997], “Further thoughts on simplicity and complexity in population projection models,” International Journal of Forecasting, 13, 557–565.
CrossRef Google Scholar
Stewart, T. R. and T. M. Leschine [1986], “Judgment and analysis in oil spill risk assessment,” Risk Analysis 6, 305–315.
CrossRef Google Scholar
Tellis, G. J. [1988], “The price elasticity of selective demand: A meta-analysis of econometric models of sales,” Journal of Marketing Research, 25, 331–341.
CrossRef Google Scholar
Witt, S. F. and C. A. Witt [1995], “Forecasting tourism demand: A review of empirical research,” International Journal of Forecasting, 11, 447–475.
CrossRef Google Scholar
Wittink, D. R. and T. Bergestuen [2001], “Forecasting with conjoint analysis,” in J. S. Armstrong [ed.], Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Google Scholar
Wright, M. and P. Gendall [1999], “Making survey-based price experiments more accurate,” Journal of the Market Research Society, 41, [2] 245–249.
Google Scholar
Yokum, T. and J. S. Armstrong [1995], “Beyond accuracy: Comparison of criteria used to select forecasting methods,” International Journal of Forecasting, 11, 591–597. Full text at hops.wharton.upenn.edu/forecast
CrossRef Google Scholar
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Armstrong, J.S. [2001]. Selecting Forecasting Methods. In: Armstrong, J.S. [eds] Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. //doi.org/10.1007/978-0-306-47630-3_16