Ben taieb, Souhaib ; Université de Mons - UMONS > Faculté des Sciences > Service Big Data and Machine Learning
Bontempi, Gianluca; Machine Learning Group, Département d'Informatique, Université Libre de Bruxelles, Belgium
Atiya, Amir F.; Faculty of Engineering, Cairo University, Giza, Egypt
Sorjamaa, Antti; Environmental and Industrial Machine Learning Group, Adaptive Informatics Research Centre, Altoo University School of Science, Finland
Language :
English
Title :
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
D.W. Aha Lazy learning 1997 Kluwer Academic Publishers Norwell, MA, USA ISBN: 0-7923-4584-3
N.K. Ahmed, A.F. Atiya, N. El Gayar, and H. El-Shishiny An empirical comparison of machine learning models for time series forecasting Econometric Reviews (to appear) 29 5-6 2010
D.M. Allen The relationship between variable selection and data agumentation and a method for prediction Technometrics 16 1 1974 125 127 ISSN: 00401706.
E. Alpaydin Introduction to machine learning 2nd ed. Adaptive computation and machine learning 2010 The MIT Press ISBN: 978-0-262-01243-0. .
U. Anders, and O. Korn Model selection in neural networks Neural Networks 12 2 1999 309 323 ISSN: 0893-6080
R.R. Andrawis, Amir F. Atiya, and H. El-Shishiny Combination of long term and short term forecasts, with application to tourism demand forecasting International Journal of Forecasting 27 3 2011 870 886
Andrawis, R. R.; Atiya, A. F.; & El-Shishiny, H. (in press). Forecast combinations of computational intelligence and linear models for the nn5 time series forecasting competition. International Journal of Forecasting (Corrected Proof). ISSN: 0169-2070. .
A. Atiya, S.M. El-shoura, S.I. Shaheen, and M.S. El-sherif A comparison between neural-network forecasting techniques - Case study: River flow forecasting IEEE Transactions on Neural Networks 10 1999 402 409
C.G. Atkeson, A.W. Moore, and S. Schaal Locally weighted learning Artificial Intelligence Review 11 1-5 1997 11 73
J.M. Bates, and C.W.J. Granger The combination of forecasts OR 20 4 1969 451 468 ISSN: 14732858.
Ben Taieb, S.; Bontempi, G.; Sorjamaa, A.; & Lendasse, A. (2009). Long-term prediction of time series by combining direct and mimo strategies. In International joint conference on neural networks. .
S. Ben Taieb, A. Sorjamaa, and G. Bontempi Multiple-output modeling for multi-step-ahead time series forecasting Neurocomputing 73 10-12 2010 1950 1957 ISSN: 0925-2312. (Subspace learning/selected papers from the european symposium on time series prediction)
Birattari, B.; & Bersini, M. (1997). Lazy learning for local modeling and control design. .
M. Birattari, G. Bontempi, and H. Bersini Lazy learning meets the recursive least-squares algorithm M.S. Kearns, S.A. Solla, D.A. Cohn, NIPS Vol. 11 1999 MIT Press Cambridge 375 381
Bontempi, G. (1999). Local learning techniques for modeling, prediction and control. Ph.d.; IRIDIA-Université Libre de Bruxelles, BELGIUM.
Bontempi, G. (2008). Long term time series prediction with multi-input multi-output local learning. In Proceedings of the 2nd European symposium on time series prediction (TSP), ESTSP08, Helsinki, Finland (pp. 145-154).
G. Bontempi, and S. Ben Taieb Conditionally dependent strategies for multiple-step-ahead prediction in local learning International Journal of Forecasting 27 3 2011 689 699
G. Bontempi, M. Birattari, and H. Bersini Local learning for iterated time-series prediction I. Bratko, S. Dzeroski, Machine learning: Proceedings of the sixteenth international conference 1999 Morgan Kaufmann Publishers San Francisco, CA 32 38
Bontempi, G.; Birattari, M.; & Bersini, H. (1998). Lazy learning for iterated time series prediction. In J.A.K. Suykens, & J. Vandewalle (Eds.), Proceedings of the international workshop on advanced black-box techniques for nonlinear modeling (pp. 62-68). Belgium: Katholieke Universiteit Leuven.
L. Breiman, and J.H. Friedman Predicting multivariate responses in multiple linear regression Journal of the Royal Statistical Society, Series B 59 1 1997 3 54
L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth International Group Belmont, CA
M. Casdagli, S. Eubank, J.D. Farmer, and J. Gibson State space reconstruction in the presence of noise Physica D 51 1991 52 98
O. Chapelle, and V. Vapnik Model selection for support vector machines Advances in neural information processing systems Vol. 12 2000 MIT Press
H. Cheng, P.-N. Tan, J. Gao, and J. Scripps Multistep-ahead time series prediction W.K. Ng, M. Kitsuregawa, J. Li, K. Chang, PAKDD Lecture notes in computer science Vol. 3918 2006 Springer 765 774 ISBN: 3-540-33206-5
R.T. Clemen Combining forecasts: A review and annotated bibliography International Journal of Forecasting 5 4 1989 559 583 ISSN: 0169-2070
M.P. Clements, P.H. Franses, and N.R. Swanson Forecasting economic and financial time-series with non-linear models International Journal of Forecasting 20 2 2004 169 183 ISSN: 0169-207
W.S. Cleveland, S.J. Devlin, and E. Grosse Regression by local fitting: Methods, properties, and computational algorithms Journal of Econometrics 37 1 1988 87 114 ISSN: 0304-4076.
S.F. Crone Mining the past to determine the future: Comments International Journal of Forecasting 25 3 2009 456 460 ISSN: 0169-2070. (Special section: time series monitoring).
Crone, Sven.; 2009a. NN3 Forecasting Competition. http://www.neural- forecasting-competition.com/NN3/index.htm, a. Last update 26/05/2009. Visited on 05/07/2010.
Crone, Sven.; 2009b. NN5 Forecasting Competition. http://www.neural- forecasting-competition.com/NN5/index.htm, b. Last update 27/05/2009. Visited on 05/07/2010.
Crone, S. F.; & Kourentzes, N. (2009). Input-variable specification for neural networks: an analysis of forecasting low and high time series frequency. In Proceedings of the 2009 international joint conference on Neural Networks, IJCNN'09 Piscataway, NJ, USA (pp. 3221-3228). IEEE Press. ISBN: 978-1-4244-3549-4. .
B. Curry, and P.H. Morga Model selection in neural networks: Some difficulties European Journal of Operational Research 170 2 2006 567 577 URL http://ideas.repec.org/a/eee/ejores/v170y2006i2p567-577.html
J.G. De Gooijer, and R.J. Hyndman 25 years of time series forecasting International Journal of Forecasting 22 3 2006 443 473 ISSN: 0169-2070
J.G. De Gooijer, and K. Kumar Some recent developments in non-linear time series modelling, testing, and forecasting International Journal of Forecasting 8 2 1992 135 156 ISSN: 0169-2070.
J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 ISSN: 1532-4435
R.F. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation Econometrica 50 4 1982 987 1007 ISSN: 00129682.
J. Fan, and I. Gijbels Adaptive order polynomial fitting: Bandwidth robustification and bias reduction Journal of Computational and Graphical Statistics 4 1995 213 227
M. Friedman The use of ranks to avoid the assumption of normality implicit in the analysis of variance Journal of the American Statistical Association 32 200 1937 675 701 ISSN: 01621459.
M. Friedman A comparison of alternative tests of significance for the problem of m rankings The Annals of Mathematical Statistics 11 1 1940 86 92 ISSN: 00034851.
S. Garca, and F. Herrera An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons Journal of Machine Learning Research 9 2009 2677 2694 URL http://www.jmlr.org/papers/ volume9/garcia08a/garcia08a.pdf
Guillén, A.; Sovilj, D.; Mateo, F.; Rojas, I.; & Lendasse, A. (2008). New methodologies based on delta test for variable selection in regression problems. In Workshop on parallel architectures and bioinspired algorithms, Toronto, Canada.
C. Hamzacebi, D. Akay, and F. Kutay Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting Expert Systems with Applications 36 2, Part 2 2009 3839 3844 ISSN: 0957-4174.
D. Hand Mining the past to determine the future: Problems and possibilities International Journal of Forecasting 2008 ISSN: 01692070.
T. Hastie, R. Tibshirani, and J. Friedman The elements of statistical learning: data mining* inference and prediction 2nd ed. 2009 Springer URL http://www.stat.stanford.edu/tibs/ElemStatLearn/
S. Hylleberg Modelling seasonality 2nd ed. 1992 Oxford University Press Oxford, UK
R.L. Iman, and J.M. Davenport Approximations of the critical region of the friedman statistic Communications in Statistics 1980 571 595
R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton Adaptive Mixtures of Local Experts Neural Computation 3 1 1991 79 87 ISSN: 08997667.
M.J. Jordan, and R.A. Jacobs Hierarchical mixtures of experts and the em algorithm Neural Computation 6 1994 181 214
H. Kantz, and T. Schreiber Nonlinear time series analysis 2004 Cambridge University Press New York, NY, USA
D.M. Kline Methods for multi-step time series forecasting with neural networks G.P. Zhang, Neural networks in business forecasting 2004 Information Science Publishing 226 250
Lapedes, A.; & Farber, R. (1987). Nonlinear signal processing using neural networks: prediction and system modelling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM.
Lendasse, A. (Ed.) (2007). ESTSP 2007: Proceedings. ISBN: 978-951-22-8601-0.
Liitiäinen, E.; & Lendasse, A. (2007). Variable scaling for time series prediction: Application to the ESTSP07 and the NN3 forecasting competitions. In IJCNN 2007, international joint conference on neural networks, Orlando, Florida, USA (pp. 2812 - 2816). Eau Claire, Wisconsin, USA: Documation LLC.
S. Makridakis, S.C. Wheelwright, and R.J. Hyndman Forecasting: Methods and Applications 1998 John Wiley & Sons
O. Maron, and A.W. Moore The racing algorithm: Model selection for lazy learners Artificial Intelligence Review 11 1 1997 193 225
F. Mateo, and A. Lendasse A variable selection approach based on the delta test for extreme learning machine models M. Verleysen, Proceedings of the European symposium on time series prediction 2008 d-side publ. Evere, Belgium 57 66
F. Mateo, and D. Sovilj Approximate k-NN delta test minimization method using genetic algorithms: Application to time series Neurocomputing 73 10-12 2010 2017 2029
J.M. Matías Multi-output nonparametric regression C. Bento, A. Cardoso, G. Dias, EPIA Lecture notes in computer science Vol. 3808 2005 Springer 288 292 ISBN: 3-540-30737-0
McNames, J. (1998). A nearest trajectory strategy for time series prediction. In Proceedings of the internationalworkshop on advanced black-box techniques for nonlinear modeling, Belgium (pp. 112-128). K.U. Leuven.
C.A. Micchelli, and M.A. Pontil On learning vector-valued functions Neural Computation 17 1 2005 177 204 ISSN: 0899-7667
T.M. Mitchel Machine learning 1997 McGraw-Hill New York
D.C. Montgomery, E.A. Peck, and G.G. Vining Introduction to Linear Regression Analysis 4th ed. 2006 Wiley & Sons Hoboken ISBN: 0471754951. .
J. Moody, and C.J. Darken Fast learning in networks of locally-tuned processing units Neural Computation 1 2 1989 281 294
Murray-Smith, R. (1994). A local model network approach to nonlinear modelling. PhD thesis, Department of Computer Science, University of Strathclyde, Strathclyde, UK.
M. Nelson, T. Hill, W. Remus, and M. O'Connor Time series forecasting using neural networks: should the data be deseasonalized first? Journal of Forecasting 18 5 1999 359 367 URL http://www.sciencedirect.com/science/article/ B6V92-469244K-1/2/cb5cbac7df80324a85e47c96f4a1e290
A.K. Palit, and D. Popovic Computational intelligence in time series forecasting: Theory and engineering applications (Advances in industrial control) 2005 Springer-Verlag New York, Inc. Secaucus, NJ ISBN: 1852339489
H. Pi, and C. Peterson Finding the embedding dimension and variable dependencies in time series Neural Computation 6 1994 509 520 ISSN: 0899-7667.
R. Poggio, and F. Girosi Regularization algorithms for learning that are equivalent to multilayer networks Science 247 1990 978 982
D.S. Poskitt, and A.R. Tremayne The selection and use of linear and bilinear time series models International Journal of Forecasting 2 1 1986 101 114 ISSN: 0169-2070
S. Price Mining the past to determine the future: Comments International Journal of Forecasting 25 3 2009 452 455
Raudys, S.; & Zliobaite, I. (2006). The multi-agent system for prediction of financial time series. In Artificial intelligence and soft computing, ICAISC 2006 (pp. 653-662).
D.E. Rumelhart, G.E. Hinton, and R.K. Williams Learning representations by backpropagating errors Nature 323 9 1986 533 536
D. Ruppert, S.J. Sheather, and M.P. Wand An effective bandwidth selector for local least squares regression Journal of the American Statistical Association 90 432 1995 1257 1270 ISSN: 01621459.
E. Saad, D. Prokhorov, and D. Wunsch Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks IEEE Transactions on Neural Networks 9 6 1998 1456 1470
T. Sauer Time series prediction by using delay coordinate embedding A.S. Weigend, N.A. Gershenfeld, Time series prediction: Forecasting the future and understanding the past 1994 Addison Wesley Harlow, UK 175 193
R.E. Schapire, Y. Freund, P. Bartlett, and W.S. Lee Boosting the margin: A new explanation for the effectiveness of voting methods Annals of Statistics 26 5 1998 1651 1686 ISSN: 00905364.
G.A.F. Seber, and C.J. Wild Nonlinear regression 1989 Wiley New York
Sorjamaa, A.; & Lendasse, A. (2006). Time series prediction using dirrec strategy. In M. Verleysen (Ed.), ESANN06, European symposium on artificial neural networks, Bruges, Belgium (pp. 143-148).
A. Sorjamaa, J. Hao, N. Reyhani, Y. Ji, and A. Lendasse Methodology for long-term prediction of time series Neurocomputing 70 16-18 2007 2861 2869
T. Takagi, and M. Sugeno Fuzzy identification of systems and its applications to modeling and control IEEE Transactions on Systems, Man, and Cybernetics 15 1 1985 116 132
L.J. Tashman Out-of-sample tests of forecasting accuracy: An analysis and review International Journal of Forecasting 16 4 2000 437 450 ISSN: 0169-2070
G.C. Tiao, and R.S. Tsay Some advances in non-linear and adaptive modelling in time-series Journal of Forecasting 13 2 1994 109 131
A. Timmermann Forecast combinations G. Elliott, C. Granger, A. Timmermann, Handbook of economic forecasting 2006 Elsevier Pub. 135 196
H. Tong Threshold models in Nonlinear Time Series Analysis 1983 Springer Verlag Berlin
H. Tong Non-linear Time Series: A Dynamical System Approach 1990 Oxford University Press
H. Tong, and K.S. Lim Threshold autoregression, limit cycles and cyclical data Journal of the Royal Statistical Society. Series B (Methodological) 42 3 1980 245 292 ISSN: 00359246.
V.T. Tran, B.-S. Yang, and A.C.C. Tan Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems Expert Systems with Applications 36 5 2009 9378 9387 ISSN: 0957-4174
Weigend, A. S.; & Gershenfeld, N. A. (Eds.) (1994). Time series prediction: Forecasting the future and understanding the past. .
A.S. Weigend, B.A. Huberman, and D.E. Rumelhart Predicting sunspots and exchange rates with connectionist networks M. Casdagli, S. Eubank, Nonlinear modeling and forecasting 1992 Addison-Wesley 395 432
Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University, Cambridge, MA.
P.J. Werbos Generalization of backpropagation with application to a recurrent gas market model Neural Networks 1 4 1988 339 356 ISSN: 0893-6080. .
J.D. Wichard Forecasting the nn5 time series with hybrid models International Journal of Forecasting 27 3 2011 700 707
G. Zhang, B. Eddy Patuwo, and M.Y. Hu Forecasting with artificial neural networks: The state of the art International Journal of Forecasting 14 1 1998 35 62 ISSN: 0169-2070
X. Zhang, and J. Hutchinson Simple architectures on fast machines: Practical issues in nonlinear time series prediction A.S. Weigend, N.A. Gershenfeld, Time series prediction: Forecasting the future and understanding the past 1994 Addison Wesley 219 241
G.P. Zhang, and M. Qi Neural network forecasting for seasonal and trend time series European Journal of Operational Research 160 2 2005 501 514 ISSN: 0377-2217. (Decision support systems in the internet age)
