What factors should you consider when choosing a forecasting method?
|
Part of the International Series in Operations Research & Management Science book series (ISOR,volume 30) Show
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. This is a preview of subscription content, access via your institution. Buying optionsChapter EUR 29.95 Price includes VAT (Singapore)
eBookEUR 373.43Price includes VAT (Singapore)
Softcover BookEUR 449.99Price excludes VAT (Singapore)
Hardcover BookEUR 449.99Price excludes VAT (Singapore)
Learn about institutional subscriptions PreviewUnable to display preview. Download preview PDF. Unable to display preview. Download preview PDF. 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. 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 Download references Author informationAuthors and Affiliations
Authors
Editor informationEditors and Affiliations
Rights and permissionsReprints and Permissions Copyright information© 2001 Springer Science+Business Media New York About this chapterCite this chapterArmstrong, 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. https://doi.org/10.1007/978-0-306-47630-3_16 What are the six factors considered in forecasting?Six Factors for Effective Sales Forecasting. Identify the current market situation. Every year is a different year. ... . Determine the readiness of your sales team. ... . Develop a strong sales support infrastructure. ... . Accurate job costs. ... . Factor in closing times. ... . Extrapolate from the known to the unknown.. Which method is considered best for forecasting?Geometrical increase method (or geometrical progression method):
What are the 4 basic forecasting method?While there are a wide range of frequently used quantitative budget forecasting tools, in this article we focus on four main methods: (1) straight-line, (2) moving average, (3) simple linear regression and (4) multiple linear regression.
|
