Forecast Selection and Representativeness

References

• Adya M, Collopy F, Armstrong JS, Kennedy M (2001) Automatic identification of time series features for rule-based forecasting. Internat. J. Forecasting 17(2):143–157.Crossref, Google Scholar
• Akaike H (1974) A new look at the statistical model identification. IEEE Trans. Automatic Control 19(6):716–723.Crossref, Google Scholar
• Arvan M, Fahimnia B, Reisi M, Siemsen E (2019) Integrating human judgement into quantitative forecasting methods: A review. Omega 86:237–252.Crossref, Google Scholar
• Assimakopoulos V, Nikolopoulos K (2000) The theta model: A decomposition approach to forecasting. Internat. J. Forecasting 16(4):521–530.Crossref, Google Scholar
• Blattberg R, Hoch S (1990) Database models and managerial intuition: 50% models + 50% manager. Management Sci. 36(8):887–899.Link, Google Scholar
• Box GEP, Jenkins GM, Reinsel GC (2008) Time Series Analysis: Forecasting and Control, 4th ed. (John Wiley & Sons, Inc., Hoboken, NJ).Crossref, Google Scholar
• Brau R, Aloysius J, Siemsen E (2021) Demand planning for the digital supply chain: How to integrate human judgment and predictive analytics. Working paper, Brigham Young University, Provo, UT.Google Scholar
• Burnham KP, Anderson DR (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. (Springer, New York).Google Scholar
• De Baets S, Harvey N (2020) Using judgment to select and adjust forecasts from statistical models. Eur. J. Oper. Res. 284(3):882–895.Crossref, Google Scholar
• de Oliveira EM, Cyrino Oliveira FL, Jeon J (2021) Treating and pruning: New approaches to forecasting model selection and combination using prediction intervals. Internat. J. Forecasting 37(2):547–568.Crossref, Google Scholar
• Fildes R (1989) Evaluation of aggregate and individual forecast method selection rules. Management Sci. 35(9):1056–1065.Link, Google Scholar
• Fildes R, Petropoulos F (2015) Simple vs. complex selection rules for forecasting many time series. J. Bus. Res. 68(8):1692–1701.Crossref, Google Scholar
• Flicker B (2018) Managerial insight and optimal algorithms. Working paper, University of South Carolina, Columbia, SC.Google Scholar
• Franses PH (2016) A note on the mean absolute scaled error. Internat. J. Forecasting 32(1):20–22.Crossref, Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2021) Cognitive challenges in human–AI collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Google Scholar
• Gardner ES (2006) Exponential smoothing: The state of the art—Part II. Internat. J. Forecasting 22(4):637–666.Crossref, Google Scholar
• Gardner ES, McKenzie E (1988) Model identification in exponential smoothing. J. Oper. Res. Soc. 39(9):863–867.Crossref, Google Scholar
• Genre V, Kenny G, Meyler A, Timmermann A (2013) Combining expert forecasts: Can anything beat the simple average? Internat. J. Forecasting 29(1):108–121.Crossref, Google Scholar
• Gigerenzer G, Todd P (1999) Simple Heuristics That Make Us Smart (Oxford University Press, New York).Google Scholar
• Gilliland M (2019) The value added by machine learning approaches in forecasting. Internat. J. Forecasting 36(1):161–166.Crossref, Google Scholar
• Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102(477):359–378.Crossref, Google Scholar
• Goldberg LR (1970) Man vs. model of man: A rationale, plus some evidence, for a method of improving on clinical inferences. Psych. Bull. 73(6):422–432.Crossref, Google Scholar
• Goodrich RL (1990) Applied Statistical Forecasting (Business Forecast Systems, Belmont, MA).Google Scholar
• Goodwin P (2019) A surprisingly useful role for judgment. Foresight Internat. J. Appl. Forecasting 54:11–12.Google Scholar
• Goodwin P, Lawton R (1999) On the asymmetry of the symmetric MAPE. Internat. J. Forecasting 15(4):405–408.Crossref, Google Scholar
• Grushka-Cockayne Y, Lichtendahl KC, Jose VRR, Winkler RL (2017) Quantile evaluation, sensitivity to bracketing, and sharing business payoffs. Oper. Res. 65(3):712–728.Link, Google Scholar
• Guerrero VM (1993) Time-series analysis supported by power transformations. J. Forecasting 12(1):37–48.Crossref, Google Scholar
• Han W, Wang X, Petropoulos F, Wang J (2019) Brain imaging and forecasting: Insights from judgmental model selection. Omega 87:1–9.Crossref, Google Scholar
• Harvey N (1995) Why are judgments less consistent in less predictable task situations? Organ. Behav. Human Decision Processes 63(3):247–263.Crossref, Google Scholar
• Harvey N, Ewart T, West R (1997) Effects of data noise on statistical judgement. Thinking Reasoning 3(2):111–132.Crossref, Google Scholar
• Hibon M, Evgeniou T (2005) To combine or not to combine: Selecting among forecasts and their combinations. Internat. J. Forecasting 21(1):15–24.Crossref, Google Scholar
• Hyndman RJ, Athanasopoulos G (2018) Forecasting: Principles and Practice, 2nd ed. (OTexts, Melbourne, Australia).Google Scholar
• Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: The forecast package for R. J. Statist. Software 27(3):1–22.Crossref, Google Scholar
• Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Internat. J. Forecasting 22(4):679–688.Crossref, Google Scholar
• Hyndman RJ, Koehler AB, Ord JK, Snyder RD (2008) Forecasting with Exponential Smoothing: The State Space Approach (Springer Verlag, Berlin).Crossref, Google Scholar
• Hyndman RJ, Koehler AB, Snyder R, Grose S (2002) A state space framework for automatic forecasting using exponential smoothing methods. Internat. J. Forecasting 18(3):439–454.Crossref, Google Scholar
• Hyndman RJ, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, et al. (2019) forecast: Forecasting functions for time series and linear models. R package version 8.9 (CRAN).Google Scholar
• Kahneman D, Tversky A (1973) On the psychology of prediction. Psych. Rev. 80(4):237–251.Crossref, Google Scholar
• Kahneman D, Rosenfeld A, Gandhi L, Blaser T (2006) Noise: How to overcome the high, hidden cost of inconsistent decision making. Harvard Bus. Rev. 94(12):36–43.Google Scholar
• Kang Y, Hyndman RJ, Smith-Miles K (2017) Visualising forecasting algorithm performance using time series instance spaces. Internat. J. Forecasting 33(2):345–358.Crossref, Google Scholar
• Kolassa S (2011) Combining exponential smoothing forecasts using Akaike weights. Internat. J. Forecasting 27(2):238–251.Crossref, Google Scholar
• Koning AJ, Franses PH, Hibon M, Stekler HO (2005) The M3 competition: Statistical tests of the results. Internat. J. Forecasting 21(3):397–409.Crossref, Google Scholar
• Kourentzes N, Barrow D, Petropoulos F (2019) Another look at forecast selection and combination: Evidence from forecast pooling. Internat. J. Production Econom. 209:226–235.Crossref, Google Scholar
• Lee YS, Siemsen E (2017) Task decomposition and newsvendor decision making. Management Sci. 63(10):3226–3245.Link, Google Scholar
• Lichtendahl KC, Grushka-Cockayne Y, Pfeifer PE (2013) The wisdom of competitive crowds. Oper. Res. 61(6):1383–1398.Link, Google Scholar
• Lyall A, Mercier P, Gstettner S (2018) The death of supply chain management. Harvard Business Review Online (June 15), https://hbr.org/2018/06/the-death-of-supply-chain-management.Google Scholar
• Makridakis S, Hibon M (2000) The M3-Competition: Results, conclusions and implications. Internat. J. Forecasting 16(4):451–476.Crossref, Google Scholar
• Makridakis S, Hogarth RM, Gaba A (2010) Dance with Chance: Making Luck Work for You (Oneworld Publications, Oxford, UK).Google Scholar
• Makridakis S, Spiliotis E, Assimakopoulos V (2020) The M4 competition: 100,000 time series and 61 forecasting methods. Internat. J. Forecasting 36(1):54–74.Crossref, Google Scholar
• Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982) The accuracy of extrapolation (time series) methods: Results of a forecasting competition. J. Forecasting 1(2):111–153.Crossref, Google Scholar
• Meade N (2000) Evidence for the selection of forecasting methods. J. Forecasting 19(6):515–535.Crossref, Google Scholar
• Montero-Manso P, Athanasopoulos G, Hyndman RJ, Talagala TS (2020) FFORMA: Feature-based forecast model averaging. Internat. J. Forecasting 36(1):86–92.Crossref, Google Scholar
• Nikolopoulos K, Petropoulos F (2018) Forecasting for big data: Does suboptimality matter? Comput. Oper. Res. 98:322–329.Crossref, Google Scholar
• Ord K, Fildes R, Kourentzes N (2017) Principles of Business Forecasting, 2nd ed. (Wessex, Inc.).Google Scholar
• Petropoulos F, Wang X, Disney SM (2019) The inventory performance of forecasting methods: Evidence from the M3 competition data. Internat. J. Forecasting 35(1):251–265.Crossref, Google Scholar
• Petropoulos F, Kourentzes N, Nikolopoulos K, Siemsen E (2018) Judgmental selection of forecasting models. J. Oper. Management 60:34–46.Crossref, Google Scholar
• Petropoulos F, Makridakis S, Assimakopoulos V, Nikolopoulos K (2014) ‘Horses for courses’ in demand forecasting. Eur. J. Oper. Res. 237(1):152–163.Crossref, Google Scholar
• Rouba I, Kim S-H, Tong J (2021) Eliciting human judgement for prediction algorithms. Management Sci. 67(4):2314–2325.Google Scholar
• Schwarz G (1978) Estimating the dimension of a model. Ann. Statist. 6(2):461–464.Crossref, Google Scholar
• Shah C (1997) Model selection in univariate time series forecasting using discriminant analysis. Internat. J. Forecasting 13(4):489–500.Crossref, Google Scholar
• Siemsen E, Aloysius J (2020) Supply chain analytics and the evolving work of supply chain managers. Association for Supply Chain Management White Paper.Google Scholar
• Spiliotis E, Kouloumos A, Assimakopoulos V, Makridakis S (2020) Are forecasting competitions data representative of the reality? Internat. J. Forecasting 36(1):37–53.Crossref, Google Scholar
• Sugiura N (1978) Further analysts of the data by Akaike’ s information criterion and the finite corrections. Comm. Statist. Theory Methods 7(1):13–26.Crossref, Google Scholar
• Svetunkov I (2019) smooth: Forecasting Using State Space Models. R package version 2.5.0 (CRAN).Google Scholar
• Svetunkov I, Petropoulos F (2018) Old dog, new tricks: A modelling view of simple moving averages. Internat. J. Production Res. 56(18):6034–6047.Crossref, Google Scholar
• Talagala TS, Hyndman RJ, Athanasopoulos G (2018) Meta-learning how to forecast time series. Technical report, Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia.Google Scholar
• Taleb NN (2008) The Black Swan: The Impact of the Highly Improbable (Penguin Random House, New York).Google Scholar
• Tashman LJ (2000) Out-of-sample tests of forecasting accuracy: An analysis and review. Internat. J. Forecasting 16(4):437–450.Crossref, Google Scholar
• Taylor JW (2008) Exponentially weighted information criteria for selecting among forecasting models. Internat. J. Forecasting 24(3):513–524.Crossref, Google Scholar
• Timmermann A (2006) Forecast combinations. Elliott G, Timmermann A, eds. Handbook of Economic Forecasting, 1st ed. (Elsevier, Amsterdam), 1 135–196.Google Scholar
• van Donselaar KH, Gaur V, van Woensel T, Broekmeulen RAC, Fransoo J (2010) Ordering behavior in retail stores and implications for automated replenishment. Management Sci. 56(5):766–784.Link, Google Scholar
• Weller M, Crone S (2012) Supply chain forecasting: Best practices & benchmarking study. Technical report, Lancaster Centre for Forecasting, Lancaster University, Lancaster, UK.Google Scholar