Demand forecasting by temporal aggregation

Naval Research Logistics

Volumn 60, Issue 6, 2013, Pages 479-498

  Rostami Tabar, Bahman ^a,c   Babai, M Zied ^a   Syntetos, Aris ^b   Ducq, Yves ^c  

^a KEDGE BUSINESS SCHOOL   (France)

^b CARDIFF UNIVERSITY   (United Kingdom)

^c UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

demand forecasting; single exponential smoothing; stationary processes; temporal aggregation

Indexed keywords

DEMAND FORECASTING; EXPONENTIAL SMOOTHING; FIRST-ORDER AUTOREGRESSIVE PROCESS; FORECASTING PERFORMANCE; MOVING AVERAGE PROCESS; NUMERICAL INVESTIGATIONS; STATIONARY PROCESS; TEMPORAL AGGREGATION;

AGGREGATES; RANDOM PROCESSES;

FORECASTING;

EID: 84882732952     PISSN: 0894069X     EISSN: 15206750     Source Type: Journal    
DOI: 10.1002/nav.21546     Document Type: Article

References (41)

1. Y. Acar, and, E.S. Gardner, Forecasting method selection in a global supply chain, Int J Forecast 28 (2012), 842-848.
2. D.J. Aigner, and, S.M. Goldfeld, Estimation and prediction from aggregate data when aggregates are measured more accurately than their components, Econometrica 42 (1974), 113-134.
3. T. Amemiya, and, R.Y. Wu, The effect of aggregation on prediction in the autoregressive model, J Am Stat Assoc 67 (1972), 628-632.
4. G. Athanasopoulos, R.J. Hyndman, H. Song, and, D.C. Wu, The tourism forecasting competition, Int J Forecast 27 (2011), 822-844.
5. M.Z. Babai, M.M. Ali, and, K. Nikolopoulos, Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis, OMEGA 40 (2012), 713-721.
6. J.E. Boylan, A.A. Syntetos, and, G.C. Karakostas, Classification for forecasting and stock control: A case study, J Oper Res Soc 59 (2008), 473-481.
7. K. Brannas, J. Hellstrom, and, J. Nordstrom, A new approach to modelling and forecasting monthly guest nights in hotels, Int J Forecast 18 (2002), 19-30.
8. K.R.W. Brewer, Some consequences of temporal aggregation and systematic sampling for arma and armax models, J Econom 1 (1973), 133-154.
9. A. Chen, and, J. Blue, Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands, Int J Product Econ 128, (2010) 586-602.
10. A. Chen, C.H. Hsu, and, J. Blue, Demand planning approaches to aggregating and forecasting interrelated demands for safety stock and backup capacity planning, Int J Prod Res 45 (2007), 2269-2294.
11. F. Chen, J.K. Ryan, and, D. Simchi-Levi, The impact of exponential smoothing forecasts on the bullwhip effect, Nav Res Log (NRL) 47 (2000), 269-286.
12. J.D. Croston, Forecasting and stock control for intermittent demands, Oper Res Q (1970-1977) 23 (1972), 289-303.
13. N. Erkip, W.H. Hausman, and, S. Nahmias, Optimal centralized ordering policies in multi-echelon inventory systems with correlated demands, Manag Sci 36 (1990), 381-392.
14. E.S. Gardner, Jr., Evaluating forecast performance in an inventory control system, Manag Sci 36 (1990), 490-499.
15. E.S. Gardner, Jr., Exponential smoothing: The state of the art-Part ii, Int J Forecast 22 (2006), 637-666.
16. E.S. Gardner, Jr., E. McKenzie, Forecasting trends in time series, Manag Sci 31 (1985), 1237-1237.
17. H.L. Lee, V. Padmanabhan, and, S. Whang, Information distortion in a supply chain: The bullwhip effect, Manag Sci 43 (1997), 546-558.
18. H.L. Lee, K.C. So, and, C.S. Tang, The value of information sharing in a two-level supply chain, Manag Sci 46 (2000), 626-643.
19. I. Luna, and, R. Ballini, Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting, Int J Forecast 27 (2011), 708-724.
20. S. Makridakis, and, M. Hibon, The m3-competition: Results, conclusions and implications, Int J Forecast 16 (2000), 451-476.
21. M. Mohammadipour, and, J.E. Boylan, Forecast horizon aggregation in integer autoregressive moving average (inarma) models, OMEGA 40 (2012), 703-712.
22. K. Nikolopoulos, A.A. Syntetos, J. Boylan, F. Petropoulos, and, V. Assimakopoulos, An aggregate-disaggregate intermittent demand approach (adida) to forecasting: An empirical proposition and analysis, J Oper Res Soc 62 (2011), 544-554.
23. N. Sariaslan, The effect of temporal aggregation on univariate time series analysis, Department of Statistics, Middle East Technical University, 2010, p. 150.
24. A. Silvestrini, and, D. Veredas, Temporal aggregation of univariate and multivariate time series models: A survey, J Econom Surveys 22 (2008), 458-497.
25. L.R. Souza, and, J. Smith, Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: A monte-carlo study, Int J Forecast 20 (2004), 487-502.
26. G.P. Spithourakis, F. Petropoulos, V. Assimakopoulos, M.Z. Babai, and, K. Nikolopoulos, Improving the performance of popular supply chain forecasting techniques, Supply Chain Forum Int J 12 (2011), 16-25.
27. G.P. Spithourakis, F. Petropoulos, K. Nikolopoulos, and, V. Assimakopoulos, A systemic view of the adida framework, IMA J Manag Math (2012), doi: 10.1093/imaman/dps031.
28. D.O. Stram, and, W.W.S. Wei, Temporal aggregation in the arima process, J Time Ser Anal 7 (1986), 279-292.
29. A.A. Syntetos, and, J.E. Boylan, The accuracy of intermittent demand estimates, Int J Forecast 21 (2005), 303-314.
30. A.A. Syntetos, Forecasting of intermittent demand, Ph D, Business School, Buckinghamshire Chilterns University College, Brunel University, London, UK, 2001.
31. J.W. Taylor, Exponential smoothing with a damped multiplicative trend, Int J Forecast 19 (2003), 715-725.
32. H. Theil, Linear aggregation of economic relations, North-Holland Publishing Company, Amsterdam, Netherlands, 1954.
33. G.C. Tiao, Asymptotic behaviour of temporal aggregates of time series, Biometrika 59 (1972), 525-531.
34. W.W.S. Wei, Time series analysis: Univariate and multivariate methods, Addison-Wesley Publishing Company, Boston, 2006.
35. U. Wemmerlov, and, D.C. Whybark, Lot-sizing under uncertainty in a rolling schedule environment, Int J Prod Res 22 (1984), 467-484.
36. H. Widiarta, S. Viswanathan, and, R. Piplani, On the effectiveness of top-down strategy for forecasting autoregressive demands, Nav Res Log (NRL) 54 (2007), 176-188.
37. H. Widiarta, S. Viswanathan, and, R. Piplani, Forecasting item-level demands: An analytical evaluation of top-down versus bottom-up forecasting in a production-planning framework, IMA J Manag Math 19 (2008), 207-218.
38. H. Widiarta, S. Viswanathan, and, R. Piplani, Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework, Int J Prod Econ 118 (2009), 87-94.
39. T.R. Willemain, C.N. Smart, J.H. Shockor, and, P.A. DeSautels, Forecasting intermittent demand in manufacturing: A comparative evaluation of croston's method, Int J Forecast 10 (1994), 529-538.
40. G. Yehuda, and, G. Zvi, Is aggregation necessarily bad?, Rev Econ Stat 42 (1960), 1-13.
41. G. Zotteri, and, M. Kalchschmidt, A model for selecting the appropriate level of aggregation in forecasting processes, Int J Prod Econ 108 (2007), 74-83.