Y. Acar and E. S. Gardner, Forecasting method selection in a global supply chain, International Journal of Forecasting, vol.28, issue.4, pp.842-848, 2012.
DOI : 10.1016/j.ijforecast.2011.11.003

D. J. Aigner and S. M. Goldfeld, Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components, Econometrica, vol.42, issue.1, pp.113-134, 1974.
DOI : 10.2307/1913689

T. Amemiya and R. Y. Wu, The Effect of Aggregation on Prediction in the Autoregressive Model, Journal of the American Statistical Association, vol.67, issue.339, pp.628-632, 1972.
DOI : 10.1080/01621459.1967.10482922

O. D. Anderson, On a lemma associated with Box, Jenkins and Granger, Journal of Econometrics, vol.3, issue.2, pp.151-156, 1975.
DOI : 10.1016/0304-4076(75)90043-3

G. Athanasopoulos, R. J. Hyndman, H. Song, and D. C. Wu, The tourism forecasting competition, International Journal of Forecasting, vol.27, issue.3, pp.822-844, 2011.
DOI : 10.1016/j.ijforecast.2010.04.009

M. Z. Babai, M. M. Ali, and K. Nikolopoulos, Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis, Omega, vol.40, issue.6, pp.713-721, 2012.
DOI : 10.1016/j.omega.2011.09.004

A. Barnea and J. Lakonishok, AN ANALYSIS OF THE USEFULNESS OF DISAGGREGATED ACCOUNTING DATA FOR FORECASTS OF CORPORATE PERFORMANCE, Decision Sciences, vol.20, issue.1, pp.17-26, 1980.
DOI : 10.2307/3003496

E. Bartezzaghi, R. Verganti, and G. Zotteri, Measuring the impact of asymmetric demand distributions on inventories, International Journal of Production Economics, vol.60, issue.61, pp.395-404, 1999.
DOI : 10.1016/S0925-5273(98)00193-5

C. Bonomo, Forecasting from the Center of the Supply Chain, Journal of Business Forecasting, pp.3-9, 2003.

G. Box, G. M. Jenkins, and G. Reinsel, Time Series Analysis: Forecasting & Control, 2008.
DOI : 10.1002/9781118619193

K. Bozos and K. Nikolopoulos, Forecasting the value effect of seasoned equity offering announcements, European Journal of Operational Research, vol.214, issue.2, pp.418-427, 2011.
DOI : 10.1016/j.ejor.2011.04.007

K. Brannas, J. Hellstrom, and J. Nordstrom, A new approach to modelling and forecasting monthly guest nights in hotels, International Journal of Forecasting, vol.18, issue.1, pp.19-30, 2002.
DOI : 10.1016/S0169-2070(01)00104-2

K. R. Brewer, Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models, Journal of Econometrics, vol.1, issue.2, pp.133-154, 1973.
DOI : 10.1016/0304-4076(73)90015-8

A. Chen and J. Blue, Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands, International Journal of Production Economics, vol.128, issue.2, pp.586-602, 2010.
DOI : 10.1016/j.ijpe.2010.07.006

URL : https://hal.archives-ouvertes.fr/emse-01098365

A. Chen, C. H. Hsu, and J. Blue, Demand planning approaches to aggregating and forecasting interrelated demands for safety stock and backup capacity planning, International Journal of Production Research, vol.32, issue.10, pp.2269-2294, 2007.
DOI : 10.2307/1391772

URL : https://hal.archives-ouvertes.fr/emse-01098367

F. Chen, J. K. Ryan, and D. Simchi-levi, The impact of exponential smoothing forecasts on the bullwhip effect, Naval Research Logistics, vol.35, issue.4, pp.269-286, 2000.
DOI : 10.1002/(SICI)1520-6750(200006)47:4<269::AID-NAV1>3.0.CO;2-Q

H. Chen and J. E. Boylan, Use of individual and group seasonal indices in subaggregate demand forecasting, Journal of the Operational Research Society, vol.5, issue.12, pp.1660-1671, 2007.
DOI : 10.1016/0169-2070(89)90010-1

H. Chen and J. E. Boylan, Empirical evidence on individual, group and shrinkage seasonal indices, International Journal of Forecasting, vol.24, issue.3, pp.525-534, 2008.
DOI : 10.1016/j.ijforecast.2008.02.005

URL : http://usir.salford.ac.uk/18023/1/IJF_paper.pdf

C. Chien, Y. Chen, and J. Peng, Demand forecast of semiconductor products based on technology diffusion, Simulation Conference, pp.2313-2322, 2008.

S. Chopra and P. Meindl, Supply Chain Management, 2001.

J. D. Croston, Forecasting and Stock Control for Intermittent Demands, Journal of the Operational Research Society, vol.23, issue.3, pp.289-303, 1970.
DOI : 10.1057/jors.1972.50

B. Dangerfield and J. Morris, An empirical evaluation of top-down and bottom-up forecasting strategies, Proceedings of the 1988 Meeting of Western Decision Sciences Institute, pp.322-324, 1988.

B. J. Dangerfield and J. S. Morris, Top-down or bottom-up: Aggregate versus disaggregate extrapolations, International Journal of Forecasting, vol.8, issue.2, pp.233-241, 1992.
DOI : 10.1016/0169-2070(92)90121-O

M. G. Dekimpe and D. M. Hanssens, Time-series models in marketing:, International Journal of Research in Marketing, vol.17, issue.2-3, pp.183-193, 2000.
DOI : 10.1016/S0167-8116(00)00014-8

F. C. Drost and T. E. Nijman, Temporal Aggregation of Garch Processes, Econometrica, vol.61, issue.4, pp.909-927, 1993.
DOI : 10.2307/2951767

T. T. Duc, H. T. Luong, and Y. Kim, A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process, European Journal of Operational Research, vol.187, issue.1, pp.243-256, 2008.
DOI : 10.1016/j.ejor.2007.03.008

D. M. Dunn, W. H. Williams, and T. L. Dechaine, Aggregate versus Subaggregate Models in Local Area Forecasting, Journal of the American Statistical Association, vol.66, issue.336, pp.68-71, 1976.
DOI : 10.1080/01621459.1976.10481478

N. Erkip, W. H. Hausman, and S. Nahmias, Optimal Centralized Ordering Policies in Multi-Echelon Inventory Systems with Correlated Demands, Management Science, vol.36, issue.3, pp.381-392, 1990.
DOI : 10.1287/mnsc.36.3.381

G. Fliedner, An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation, Computers & Operations Research, vol.26, issue.10-11, pp.1133-1149, 1999.
DOI : 10.1016/S0305-0548(99)00017-9

E. S. Gardner and . Jr, Evaluating Forecast Performance in an Inventory Control System, Management Science, vol.36, issue.4, pp.490-499, 1990.
DOI : 10.1287/mnsc.36.4.490

E. S. Gardner and . Jr, Exponential smoothing: The state of the art???Part II, International Journal of Forecasting, vol.22, issue.4, pp.637-666, 2006.
DOI : 10.1016/j.ijforecast.2006.03.005

E. S. Gardner, . Jr, and E. Mckenzie, Forecasting Trends in Time Series, Management Science, vol.31, issue.10, pp.1237-1237, 1985.
DOI : 10.1287/mnsc.31.10.1237

V. Gaur, A. Giloni, and S. Seshadri, Information Sharing in a Supply Chain Under ARMA Demand, Management Science, vol.51, issue.6, pp.961-969, 2005.
DOI : 10.1287/mnsc.1050.0385

T. Gordon, B. Dangerfield, and J. Morris, Top-down or bottom-up: Which is the best approach to forecasting?, The Journal of Business Forecasting, vol.16, pp.13-16, 1997.

C. W. Granger and M. J. Morris, Time series modeling and interpretation, Journal of the Royal Statistical Society. Series A, pp.246-257, 1976.

S. C. Graves and S. P. Willems, Optimizing Strategic Safety Stock Placement in Supply Chains, Manufacturing & Service Operations Management, pp.68-83, 2000.
DOI : 10.1287/msom.2.1.68.23267

C. W. Gross and J. E. Sohl, Disaggregation methods to expedite product line forecasting, Journal of Forecasting, vol.137, issue.3, pp.233-254, 1990.
DOI : 10.1002/for.3980090304

Y. Grunfeld and Z. Griliches, Is Aggregation Necessarily Bad?, The Review of Economics and Statistics, vol.42, issue.1, pp.1-13, 1960.
DOI : 10.2307/1926089

A. C. Harvey, Time Series Models, 1993.

R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos, and H. L. Shang, Optimal combination forecasts for hierarchical time series, Computational Statistics & Data Analysis, vol.55, issue.9, pp.2579-2589, 2011.
DOI : 10.1016/j.csda.2011.03.006

R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, 2013.

R. J. Hyndman and A. B. Koehler, Another look at measures of forecast accuracy, International Journal of Forecasting, vol.22, issue.4, pp.679-688, 2006.
DOI : 10.1016/j.ijforecast.2006.03.001

J. P. Kleijnen and W. Groenendaal, Simulation: a statistical perspective, 1992.

L. Lapide, Sales and Operations Planning Part I: The Process, The Journal of Business Forecasting, pp.17-19, 2004.

L. Lapide, Top-Down & Bottom-Up Forecasting In S&OP, The Journal of Business Forecasting, vol.25, pp.14-16, 2006.

H. L. Lee, P. , P. Whang, and S. , The bullwhip effect in supply chains, IEEE Engineering Management Review, vol.43, issue.2, pp.93-102, 1997.
DOI : 10.1109/EMR.2015.7123235

H. L. Lee, V. Padmanabhan, and S. Whang, Information Distortion in a Supply Chain: The Bullwhip Effect, Management Science, vol.43, issue.4, pp.546-558, 1997.
DOI : 10.1287/mnsc.43.4.546

H. L. Lee, K. C. So, and C. S. Tang, The Value of Information Sharing in a Two-Level Supply Chain, Management Science, vol.46, issue.5, pp.626-643, 2000.
DOI : 10.1287/mnsc.46.5.626.12047

K. H. Luiz, A. M. Pedro, and L. V. Pedro, The Effect of Overlapping Aggregation on Time Series Models: An Application to the Unemployment Rate in Brazil, Brazilian Review of Econometrics, vol.12, pp.223-241, 1992.

I. Luna and R. Ballini, Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting, International Journal of Forecasting, vol.27, issue.3, pp.708-724, 2011.
DOI : 10.1016/j.ijforecast.2010.09.006

H. Lütkepohl, Forecasting Contemporaneously Aggregated Vector ARMA Processes, Journal of Business & Economic Statistics, vol.2, pp.201-214, 1984.

S. Mahajan and O. Desai, Value of Information in a Serial Supply Chain under a Nonstationary Demand Process, Working Paper Series. Indian Institute of Management (IIMB), 2011.

S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon et al., The accuracy of extrapolation (time series) methods: Results of a forecasting competition, Journal of Forecasting, vol.27, issue.2, pp.111-153, 1982.
DOI : 10.1002/for.3980010202

S. Makridakis and M. Hibon, The M3-Competition: results, conclusions and implications, International Journal of Forecasting, vol.16, issue.4, pp.451-476, 2000.
DOI : 10.1016/S0169-2070(00)00057-1

A. Martel, M. Diaby, and F. Boctor, Multiple items procurement under stochastic nonstationary demands, European Journal of Operational Research, vol.87, issue.1, pp.74-92, 1995.
DOI : 10.1016/0377-2217(94)00019-9

M. Mohammadipour, J. Boylan, and A. Syntetos, The Application of Product-Group Seasonal Indexes to Individual Products, Foresight: The International Journal of Applied Forecasting, pp.18-24, 2012.

M. Mohammadipour and J. E. Boylan, Forecast horizon aggregation in integer autoregressive moving average (INARMA) models, Omega, vol.40, issue.6, pp.703-712, 2012.
DOI : 10.1016/j.omega.2011.08.008

S. Nahmias, Production and Operations Analysis, 1993.

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, Journal of the Operational Research Society, vol.20, issue.3, pp.544-554, 2011.
DOI : 10.1016/S0169-2070(03)00013-X

G. H. Orcutt, H. W. Watts, and J. B. Edwards, Data aggregation and information loss, The American Economic Review, vol.58, 1968.

M. Quenouille, Discrete autoregressive schemes with varying time-intervals, Metrika, vol.1, issue.1, pp.21-27, 1958.
DOI : 10.1007/BF02613390

B. Rostami-tabar, M. Z. Babai, A. Syntetos, and Y. Ducq, Demand forecasting by temporal aggregation, Naval Research Logistics (NRL), vol.108, issue.6, pp.479-498, 2013.
DOI : 10.1002/nav.21546

URL : https://hal.archives-ouvertes.fr/hal-00859853

B. Rostami-tabar, M. Z. Babai, A. Syntetos, and Y. Ducq, Forecasting aggregate ARMA(1,1) Demands: theoretical analysis of top-down versus bottom-up, the International Conference on Industrial Engineering and Systems Management, 2013.

B. Rostami-tabar, M. Z. Babai, A. Syntetos, and Y. Ducq, The Impact of Temporal Aggregation on Demand Forecasting of ARMA(1, 1) Process: Theoretical Analysis, 2013.

N. Bakhtadze, K. Chernyshov, A. Dolgui, and V. Lototsky, Manufacturing Modelling, Management, and Control, pp.1696-1701
URL : https://hal.archives-ouvertes.fr/emse-00880975

N. Sariaslan, The Effect of Temporal Aggregation on Univariate Time Series Analysis, p.150, 2010.

G. Sbrana and A. Silvestrini, Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework, International Journal of Production Economics, 2013.

C. Schluter and M. Trede, Estimating continuous-time income models, Working Paper 18, 2011.

A. B. Schwarzkopf, R. J. Tersine, and J. S. Morris, Top-down versus bottom-up forecasting strategies, International Journal of Production Research, vol.58, issue.11, pp.1833-1843, 1988.
DOI : 10.2307/2344546

E. Shlifer and R. W. Wolff, Aggregation and Proration in Forecasting, Management Science, vol.25, issue.6, 1979.
DOI : 10.1287/mnsc.25.6.594

A. Silvestrini and D. Veredas, Temporal aggregation of univariate and multivariate time series models: A survey, Temi di discussione (working papers) Bank of Italy, 2008.

L. R. Souza and J. Smith, Effects of temporal aggregation on estimates and forecasts of fractionally integrated processes: a Monte-Carlo study, International Journal of Forecasting, vol.20, issue.3, pp.487-502, 2004.
DOI : 10.1016/S0169-2070(03)00066-9

S. Georgios, P. , F. Petropoulos, V. Assimakopoulos, M. Z. Babai et al., Improving the Performance of Popular Supply Chain Forecasting Techniques Supply Chain Forum, An International Journal, vol.12, pp.16-25, 2011.

G. Spyros, S. C. Makridakis, . Wheelwright, and R. J. Hyndman, Forecasting: Methods and Applications, 1998.

D. O. Stram and W. W. Wei, TEMPORAL AGGREGATION IN THE ARIMA PROCESS, Journal of Time Series Analysis, vol.7, issue.4, pp.279-292, 1986.
DOI : 10.1093/biomet/63.3.513

A. A. Syntetos and J. E. Boylan, The accuracy of intermittent demand estimates, International Journal of Forecasting, vol.21, issue.2, pp.303-314, 2005.
DOI : 10.1016/j.ijforecast.2004.10.001

A. A. Syntetos, Forecasting of Intermittent Demand, Business School, 2001.

J. W. Taylor, Exponential smoothing with a damped multiplicative trend, International Journal of Forecasting, vol.19, issue.4, pp.715-725, 2003.
DOI : 10.1016/S0169-2070(03)00003-7

P. Teles, W. W. Wei, and N. Crato, The use of aggregate series in testing for long memory, Bulletin of the International Statistical Institute, vol.3, pp.341-342, 1999.

H. Theil, Linear Aggregation of Economic Relations, 1954.

G. C. Tiao, Asymptotic behaviour of temporal aggregates of time series, Biometrika, vol.59, issue.3, pp.525-531, 1972.
DOI : 10.1093/biomet/59.3.525

H. Tunc, O. A. Kilic, S. A. Tarim, and B. Eksioglu, The cost of using stationary inventory policies when demand is non-stationary, Omega, vol.39, issue.4, pp.410-415, 2011.
DOI : 10.1016/j.omega.2010.09.005

S. Viswanathan, H. Widiarta, and R. Piplani, Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting, IMA Journal of Management Mathematics, vol.19, issue.3, pp.275-287, 2008.
DOI : 10.1093/imaman/dpn001

L. R. Weatherford, S. E. Kimes, and D. A. Scott, Forecasting for hotel revenue management: Testing aggregation against disaggregation, The Cornell Hotel and Restaurant Administration Quarterly, vol.42, issue.4, pp.53-64, 2001.
DOI : 10.1016/S0010-8804(01)80045-8

W. W. Wei, Some Consequences of Temporal Aggregation in Seasonal Time Series Models, Seasonal Analysis of Economic Time Series. US Department of Commerce, Bureau of the Census, 1979.

W. W. Wei, Time Series Analysis: Univariate and Multivariate Methods, 2006.
DOI : 10.1093/oxfordhb/9780199934898.013.0022

A. A. Weiss, Systematic sampling and temporal aggregation in time series models, Journal of Econometrics, vol.26, issue.3, pp.271-281, 1984.
DOI : 10.1016/0304-4076(84)90022-8

U. Wemmerlov and D. C. Whybark, Lot-sizing under uncertainty in a rolling schedule environment, International Journal of Production Research, vol.7, issue.3, pp.467-484, 1984.
DOI : 10.1080/00207548408942467

H. Widiarta, S. Viswanathan, and R. Piplani, On the effectiveness of top-down strategy for forecasting autoregressive demands, Naval Research Logistics, vol.25, issue.2, pp.176-188, 2007.
DOI : 10.1002/nav.20200

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 Journal of Management Mathematics, vol.19, issue.2, pp.207-218, 2008.
DOI : 10.1093/imaman/dpm039

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, International Journal of Production Economics, vol.118, issue.1, pp.87-94, 2009.
DOI : 10.1016/j.ijpe.2008.08.013

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, International Journal of Forecasting, vol.10, issue.4, pp.529-538, 1994.
DOI : 10.1016/0169-2070(94)90021-3

G. Yehuda and G. Zvi, Is aggregation necessarily bad? Review of, Economics and Statistics, vol.42, pp.1-13, 1960.

P. Zaffaroni, Contemporaneous aggregation of GARCH processes, Journal of Time Series Analysis, vol.5, issue.4, pp.521-544, 2007.
DOI : 10.1017/S0266466604201062

X. Zhang, Evolution of ARMA Demand in Supply Chains, Manufacturing & Service Operations Management, vol.6, issue.2, pp.195-198, 2004.
DOI : 10.1287/msom.1040.0042

G. Zotteri and M. Kalchschmidt, A model for selecting the appropriate level of aggregation in forecasting processes, International Journal of Production Economics, vol.108, issue.1-2, pp.74-83, 2007.
DOI : 10.1016/j.ijpe.2006.12.030

G. Zotteri, M. Kalchschmidt, and F. Caniato, The impact of aggregation level on forecasting performance, International Journal of Production Economics, vol.93, issue.94, pp.93-94, 2005.
DOI : 10.1016/j.ijpe.2004.06.044

L. Résultats-de-cette-recherche, agrégation temporelle sont les suivantes: ? Les conditions de la supériorité des approches de l'agrégation et la non-agrégation sont identifiées. Les valeurs des points de rupture sont déterminées pour des valeurs données du niveau d'agrégation, et la constante de lissage associée à la série de la demande initiale, Il en résulte des règles théoriques montrant la performance de chaque approche aux niveaux de la comparaison désagrégée et agrégée