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Volumn 73, Issue 10-12, 2010, Pages 2006-2016

Meta-learning for time series forecasting and forecast combination

Author keywords

Diversity; Forecast combination; Forecasting; Meta learning; Time series; Time series features

Indexed keywords

COMBINATION FORECASTING; EXPERT KNOWLEDGE; FEATURE SETS; FORECAST COMBINATION; FORECASTING METHODS; FORECASTING PERFORMANCE; INFORMED DECISION; META-LEARNING APPROACH; METALEARNING; SIMPLE MODEL; TIME SERIES FORECASTING;

EID: 77952545391     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.09.020     Document Type: Article
Times cited : (224)

References (50)
  • 1
    • 0034288943 scopus 로고    scopus 로고
    • An application of rule-based forecasting to a situation lacking domain knowledge
    • Adya M., Armstrong J., Collopy F., Kennedy M. An application of rule-based forecasting to a situation lacking domain knowledge. International Journal of Forecasting 2000, 16(4):477-484.
    • (2000) International Journal of Forecasting , vol.16 , Issue.4 , pp. 477-484
    • Adya, M.1    Armstrong, J.2    Collopy, F.3    Kennedy, M.4
  • 3
    • 33747888847 scopus 로고    scopus 로고
    • Persistence in forecasting performance and conditional combination strategies
    • Aiolfi M., Timmermann A. Persistence in forecasting performance and conditional combination strategies. Journal of Econometrics 2006, 127(1-2):31-53.
    • (2006) Journal of Econometrics , vol.127 , Issue.1-2 , pp. 31-53
    • Aiolfi, M.1    Timmermann, A.2
  • 4
    • 0031141263 scopus 로고    scopus 로고
    • Combining and selecting forecasting models using rule based induction
    • Arinze B., Kim S.-L., Anandarajan M. Combining and selecting forecasting models using rule based induction. Computers & Operations Research 1997, 24(5):423-433.
    • (1997) Computers & Operations Research , vol.24 , Issue.5 , pp. 423-433
    • Arinze, B.1    Kim, S.-L.2    Anandarajan, M.3
  • 7
    • 0037361994 scopus 로고    scopus 로고
    • Ranking learning algorithms: using IBL and meta-learning on accuracy and time results
    • Brazdil P., Soares C., Pinto de Costa J. Ranking learning algorithms: using IBL and meta-learning on accuracy and time results. Machine Learning 2003, 50(3):251-277.
    • (2003) Machine Learning , vol.50 , Issue.3 , pp. 251-277
    • Brazdil, P.1    Soares, C.2    Pinto de Costa, J.3
  • 8
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors. Machine learning 1996, 24(2):123-140.
    • (1996) Machine learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 10
    • 0016522307 scopus 로고
    • A Bayesian approach to the linear combination of forecasts
    • Bunn D. A Bayesian approach to the linear combination of forecasts. Operational Research Quarterly 1975, 26(2):325-329.
    • (1975) Operational Research Quarterly , vol.26 , Issue.2 , pp. 325-329
    • Bunn, D.1
  • 12
    • 0000167087 scopus 로고
    • Rule-based forecasting: development and validation of an expert systems approach to combining time series extrapolations
    • Collopy F., Armstrong S.J. Rule-based forecasting: development and validation of an expert systems approach to combining time series extrapolations. Management Science 1992, 38(10):1394-1414.
    • (1992) Management Science , vol.38 , Issue.10 , pp. 1394-1414
    • Collopy, F.1    Armstrong, S.J.2
  • 13
    • 77952552896 scopus 로고    scopus 로고
    • NN3 Forecasting Competition [Online]. Available online [02/06/2009].
    • S. Crone, 2006/2007, NN3 Forecasting Competition [Online]. Available online [02/06/2009]. http://www.neural-forecasting-competition.com/NN3/.
    • (2006)
    • Crone, S.1
  • 14
    • 77952552307 scopus 로고    scopus 로고
    • NN5 Forecasting Competition [Online]. Available online [02/06/2009].
    • S. Crone, 2008, NN5 Forecasting Competition [Online]. Available online [02/06/2009]. http://www.neural-forecasting-competition.com/NN5/.
    • (2008)
    • Crone, S.1
  • 15
    • 77952552811 scopus 로고    scopus 로고
    • Delft Center for Systems and Control. Matlab toolbox ARMASA [Online] [13/06/2007].
    • Delft Center for Systems and Control, 2007. Matlab toolbox ARMASA [Online] [13/06/2007]. http://www.dcsc.tudelft.nl/Research/Software.
    • (2007)
  • 16
    • 0039988139 scopus 로고    scopus 로고
    • Time series forecasting with neural networks: a comparative study using the air line data
    • Faraway J., Chatfield C. Time series forecasting with neural networks: a comparative study using the air line data. Applied Statistics 1998, 47(2):231-250.
    • (1998) Applied Statistics , vol.47 , Issue.2 , pp. 231-250
    • Faraway, J.1    Chatfield, C.2
  • 17
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. The Journal of Computer and System Sciences 1997, 55(1):119-139.
    • (1997) The Journal of Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 18
    • 10444224737 scopus 로고    scopus 로고
    • Classifier selection for majority voting
    • Gabrys B., Ruta D. Classifier selection for majority voting. Information Fusion 2005, 6(1):63-81.
    • (2005) Information Fusion , vol.6 , Issue.1 , pp. 63-81
    • Gabrys, B.1    Ruta, D.2
  • 19
    • 84984477151 scopus 로고
    • Exponential smoothing: the state of the art
    • Gardner E.S. Exponential smoothing: the state of the art. Journal of Forecasting 1985, 4(1):1-28.
    • (1985) Journal of Forecasting , vol.4 , Issue.1 , pp. 1-28
    • Gardner, E.S.1
  • 20
    • 33749523557 scopus 로고    scopus 로고
    • Exponential smoothing: the state of the art-part II
    • Gardner E.S. Exponential smoothing: the state of the art-part II. International Journal of Forecasting 2006, 22(4):637-666.
    • (2006) International Journal of Forecasting , vol.22 , Issue.4 , pp. 637-666
    • Gardner, E.S.1
  • 22
    • 67649359699 scopus 로고    scopus 로고
    • Forecasting with unobserved components time series models
    • Elsevier, North-Holland, Amsterdam, G. Elliott, C. Granger, A. Timmermann (Eds.)
    • Harvey A. Forecasting with unobserved components time series models. Handbook of Economic Forecasting 2006, 327-408. Elsevier, North-Holland, Amsterdam. G. Elliott, C. Granger, A. Timmermann (Eds.).
    • (2006) Handbook of Economic Forecasting , pp. 327-408
    • Harvey, A.1
  • 24
    • 38949170600 scopus 로고    scopus 로고
    • Simple robust averages of forecasts: some empirical results
    • Jose V.R.R., Winkler R.L. Simple robust averages of forecasts: some empirical results. International Journal of Forecasting 2008, 24(1):163-169.
    • (2008) International Journal of Forecasting , vol.24 , Issue.1 , pp. 163-169
    • Jose, V.R.R.1    Winkler, R.L.2
  • 25
    • 0000153354 scopus 로고    scopus 로고
    • Noemon: design, implementation and performance results of an intelligent assistant for classifier selection
    • Kalousis A., Theoharis T. Noemon: design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis 1999, 5(3):319-337.
    • (1999) Intelligent Data Analysis , vol.5 , Issue.3 , pp. 319-337
    • Kalousis, A.1    Theoharis, T.2
  • 26
    • 77952551521 scopus 로고    scopus 로고
    • Time series prediction with multilayer perceptron, fir and elman neural networks, in: Proceedings of the World Congress on Neural Networks in San Diego, California.
    • T. Koskela, M. Lehtokangas, J. Saarinen, K. Kaski, Time series prediction with multilayer perceptron, fir and elman neural networks, in: Proceedings of the World Congress on Neural Networks in San Diego, California, 1996, pp. 491-496.
    • (1996) , pp. 491-496
    • Koskela, T.1    Lehtokangas, M.2    Saarinen, J.3    Kaski, K.4
  • 27
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles
    • Kuncheva L.I., Whitaker C.J. Measures of diversity in classifier ensembles. Machine Learning 2003, 51(2):181-207.
    • (2003) Machine Learning , vol.51 , Issue.2 , pp. 181-207
    • Kuncheva, L.I.1    Whitaker, C.J.2
  • 28
    • 77952554182 scopus 로고    scopus 로고
    • On the benefit of using time series features for choosing a forecasting method, in: Proceedings of the European Symposium on Time Series Prediction in Porvoo, Finland.
    • C. Lemke, B. Gabrys, On the benefit of using time series features for choosing a forecasting method, in: Proceedings of the European Symposium on Time Series Prediction in Porvoo, Finland, 2008, pp. 1-10.
    • (2008) , pp. 1-10
    • Lemke, C.1    Gabrys, B.2
  • 29
    • 69849086682 scopus 로고    scopus 로고
    • Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations, in: Proceedings of the IEEE Symposium Series on Computational Intelligence in Nashville, USA.
    • C. Lemke, S. Riedel, B. Gabrys, Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations, in: Proceedings of the IEEE Symposium Series on Computational Intelligence in Nashville, USA, 2009, pp. 85-91.
    • (2009) , pp. 85-91
    • Lemke, C.1    Riedel, S.2    Gabrys, B.3
  • 30
    • 77952542652 scopus 로고    scopus 로고
    • ESTSP 2008: Proceedings, Multiprint Oy/Otamedia.
    • A. Lendasse (Ed.), ESTSP 2008: Proceedings, Multiprint Oy/Otamedia, 2008.
    • (2008)
    • Lendasse, A.1
  • 31
    • 0033485370 scopus 로고    scopus 로고
    • Ensemble learning via negative correlation
    • Liu Y., Yao X. Ensemble learning via negative correlation. Neural Networks 1999, 12(10):1399-1404.
    • (1999) Neural Networks , vol.12 , Issue.10 , pp. 1399-1404
    • Liu, Y.1    Yao, X.2
  • 32
    • 28344445821 scopus 로고    scopus 로고
    • Selection of time series forecasting models based on performance information, in: Proceedings of the Fourth International Conference on Hybrid Intelligent Systems in Kitakyushu, Japan.
    • P. Maforte dos Santos, T. Ludermir, R. Cavalcante, Selection of time series forecasting models based on performance information, in: Proceedings of the Fourth International Conference on Hybrid Intelligent Systems in Kitakyushu, Japan, 2004, pp. 366-371.
    • (2004) , pp. 366-371
    • Maforte dos Santos, P.1    Ludermir, T.2    Cavalcante, R.3
  • 33
    • 0034288942 scopus 로고    scopus 로고
    • The M3-competition: results, conclusions and implications
    • Makridakis S., Hibon M. The M3-competition: results, conclusions and implications. International Journal of Forecasting 2000, 16(4):451-476.
    • (2000) International Journal of Forecasting , vol.16 , Issue.4 , pp. 451-476
    • Makridakis, S.1    Hibon, M.2
  • 36
  • 37
    • 0000278558 scopus 로고
    • Exponential forecasting: some new variations
    • Pegels C. Exponential forecasting: some new variations. Management Science 1969, 15(5):311-315.
    • (1969) Management Science , vol.15 , Issue.5 , pp. 311-315
    • Pegels, C.1
  • 38
    • 77952551581 scopus 로고    scopus 로고
    • The ssm toolbox for matlab, Technical Report, Institute of Statistical Science, Academia Sinica.
    • J.Y. Peng, J.A.D. Aston, The ssm toolbox for matlab, Technical Report, Institute of Statistical Science, Academia Sinica, 2007. http://www.stat.sinica.edu.tw/jaston/software.html.
    • (2007)
    • Peng, J.Y.1    Aston, J.A.D.2
  • 39
    • 22944486338 scopus 로고    scopus 로고
    • Using machine learning techniques to combine forecasting methods, in: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence in Cairns, Australia.
    • R. Prudencio, T. Ludermir, Using machine learning techniques to combine forecasting methods, in: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence in Cairns, Australia, 2004, pp. 1122-1127.
    • (2004) , pp. 1122-1127
    • Prudencio, R.1    Ludermir, T.2
  • 40
    • 10244243684 scopus 로고    scopus 로고
    • Meta-learning approaches to selecting time series models
    • Prudencio R.B., Ludermir T.B. Meta-learning approaches to selecting time series models. Neurocomputing 2004, 61:121-137.
    • (2004) Neurocomputing , vol.61 , pp. 121-137
    • Prudencio, R.B.1    Ludermir, T.B.2
  • 41
    • 0031537084 scopus 로고    scopus 로고
    • Model selection in univariate time series forecasting using discriminant analysis
    • Shah C. Model selection in univariate time series forecasting using discriminant analysis. International Journal of Forecasting 1997, 13(4):489-500.
    • (1997) International Journal of Forecasting , vol.13 , Issue.4 , pp. 489-500
    • Shah, C.1
  • 42
    • 0012675693 scopus 로고    scopus 로고
    • A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series
    • Oxford University Press, Oxford, R. Engle, H. White (Eds.)
    • Stock J., Watson M. A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. Cointegration, Causality and Forecasting. A Festschrift in honour of Clive W.J. Granger 2001, 1-44. Oxford University Press, Oxford. R. Engle, H. White (Eds.).
    • (2001) Cointegration, Causality and Forecasting. A Festschrift in honour of Clive W.J. Granger , pp. 1-44
    • Stock, J.1    Watson, M.2
  • 43
    • 0142023273 scopus 로고    scopus 로고
    • Exponential smoothing with a damped multiplicative trend
    • Taylor J.W. Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting 2003, 19(4):715-725.
    • (2003) International Journal of Forecasting , vol.19 , Issue.4 , pp. 715-725
    • Taylor, J.W.1
  • 44
    • 67649372714 scopus 로고    scopus 로고
    • Forecast combinations
    • Elsevier, G. Elliott, C. Granger, A. Timmermann (Eds.)
    • Timmermann A. Forecast combinations. Handbook of Economic Forecasting 2006, 135-196. Elsevier. G. Elliott, C. Granger, A. Timmermann (Eds.).
    • (2006) Handbook of Economic Forecasting , pp. 135-196
    • Timmermann, A.1
  • 45
    • 77952544497 scopus 로고    scopus 로고
    • University of Goettingen. TSTOOL software package for nonlinear time series analysis [online]. Available online [03/06/2009].
    • University of Goettingen, 2009. TSTOOL software package for nonlinear time series analysis [online]. Available online [03/06/2009]. http://www.dpi.physik.uni-goettingen.de/tstool/.
    • (2009)
  • 47
    • 0030527289 scopus 로고    scopus 로고
    • Automatic feature identification and graphical support in rule-based forecasting: a comparison
    • Vokurka R., Flores B., Pearce S. Automatic feature identification and graphical support in rule-based forecasting: a comparison. International Journal of Forecasting 1996, 12(4):495-512.
    • (1996) International Journal of Forecasting , vol.12 , Issue.4 , pp. 495-512
    • Vokurka, R.1    Flores, B.2    Pearce, S.3
  • 48
    • 67349267030 scopus 로고    scopus 로고
    • Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series
    • Wang X., Smith-Miles K., Hyndman R. Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series. Neurocomputing 2009, 72:2581-2594.
    • (2009) Neurocomputing , vol.72 , pp. 2581-2594
    • Wang, X.1    Smith-Miles, K.2    Hyndman, R.3
  • 49
    • 0000459353 scopus 로고    scopus 로고
    • The lack of a priori distinctions between learning algorithms
    • Wolpert D. The lack of a priori distinctions between learning algorithms. Neural Computation 1996, 8(7):1341-1390.
    • (1996) Neural Computation , vol.8 , Issue.7 , pp. 1341-1390
    • Wolpert, D.1
  • 50
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: the state of the art
    • Zhang G., Patuwo B., Hu M. Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting 1998, 14(1):35-62.
    • (1998) International Journal of Forecasting , vol.14 , Issue.1 , pp. 35-62
    • Zhang, G.1    Patuwo, B.2    Hu, M.3


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