메뉴 건너뛰기




Volumn 73, Issue 10-12, 2010, Pages 1923-1936

Feature selection for time series prediction - A combined filter and wrapper approach for neural networks

Author keywords

Artificial neural networks; Automatic model specification; Feature selection; Forecasting; Input variable selection; Time series prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AUTOMATIC MODELS; FEATURE SELECTION; INPUT VARIABLE SELECTION; TIME SERIES PREDICTION;

EID: 77952552084     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.01.017     Document Type: Article
Times cited : (172)

References (63)
  • 1
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: the state of the art
    • Zhang G.Q., Patuwo B.E., Hu M.Y. 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.Q.1    Patuwo, B.E.2    Hu, M.Y.3
  • 2
    • 0025751820 scopus 로고
    • Approximation capabilities of multilayer feedforward networks
    • Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Networks 1991, 4(2):251-257.
    • (1991) Neural Networks , vol.4 , Issue.2 , pp. 251-257
    • Hornik, K.1
  • 3
    • 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
  • 4
    • 0000860595 scopus 로고    scopus 로고
    • Neural network models for time series forecasts
    • Hill T., O'Connor M., Remus W. Neural network models for time series forecasts. Management Science 1996, 42(7):1082-1092.
    • (1996) Management Science , vol.42 , Issue.7 , pp. 1082-1092
    • Hill, T.1    O'Connor, M.2    Remus, W.3
  • 5
    • 4344591889 scopus 로고    scopus 로고
    • Neural network forecasting for seasonal and trend time series
    • Zhang G.P., Qi M. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 2005, 160(2):501-514.
    • (2005) European Journal of Operational Research , vol.160 , Issue.2 , pp. 501-514
    • Zhang, G.P.1    Qi, M.2
  • 6
    • 33745941548 scopus 로고    scopus 로고
    • Findings from evidence-based forecasting: methods for reducing forecast error
    • Armstrong J.S. Findings from evidence-based forecasting: methods for reducing forecast error. International Journal of Forecasting 2006, 22(3):583-598.
    • (2006) International Journal of Forecasting , vol.22 , Issue.3 , pp. 583-598
    • Armstrong, J.S.1
  • 7
    • 0033105287 scopus 로고    scopus 로고
    • Model selection in neural networks
    • Anders U., Korn O. Model selection in neural networks. Neural Networks 1999, 12(2):309-323.
    • (1999) Neural Networks , vol.12 , Issue.2 , pp. 309-323
    • Anders, U.1    Korn, O.2
  • 8
    • 31744444183 scopus 로고    scopus 로고
    • A comparison of univariate methods for forecasting electricity demand up to a day ahead
    • Taylor J.W., de Menezes L.M., McSharry P.E. A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting 2006, 22(1):1-16.
    • (2006) International Journal of Forecasting , vol.22 , Issue.1 , pp. 1-16
    • Taylor, J.W.1    de Menezes, L.M.2    McSharry, P.E.3
  • 9
    • 26844455787 scopus 로고    scopus 로고
    • Model selection in neural networks: some difficulties
    • Curry B., Morgan P.H. Model selection in neural networks: some difficulties. European Journal of Operational Research 2006, 170(2):567-577.
    • (2006) European Journal of Operational Research , vol.170 , Issue.2 , pp. 567-577
    • Curry, B.1    Morgan, P.H.2
  • 13
    • 0029714384 scopus 로고    scopus 로고
    • Neural networks for time series processing
    • Dorffner G. Neural networks for time series processing. Neural Network World 1996, 6(4):447-468.
    • (1996) Neural Network World , vol.6 , Issue.4 , pp. 447-468
    • Dorffner, G.1
  • 14
    • 0002748218 scopus 로고
    • How neural nets work
    • American Institute of Physics, New York, D.Z. Anderson (Ed.)
    • Lapedes A., Farber R. How neural nets work. Neural Information Processing Systems 1988, 442-456. American Institute of Physics, New York. D.Z. Anderson (Ed.).
    • (1988) Neural Information Processing Systems , pp. 442-456
    • Lapedes, A.1    Farber, R.2
  • 15
    • 0001181242 scopus 로고    scopus 로고
    • Improving the pricing of options: a neural network approach
    • Anders U., Korn O., Schmitt C. Improving the pricing of options: a neural network approach. Journal of Forecasting 1998, 17(5-6):369-388.
    • (1998) Journal of Forecasting , vol.17 , Issue.5-6 , pp. 369-388
    • Anders, U.1    Korn, O.2    Schmitt, C.3
  • 16
    • 84979404783 scopus 로고
    • Backpropagation in time-series forecasting
    • Lachtermacher G., Fuller J.D. Backpropagation in time-series forecasting. Journal of Forecasting 1995, 14(4):381-393.
    • (1995) Journal of Forecasting , vol.14 , Issue.4 , pp. 381-393
    • Lachtermacher, G.1    Fuller, J.D.2
  • 17
  • 18
    • 10644282144 scopus 로고    scopus 로고
    • The accuracy of a procedural approach to specifying feedforward neural networks for forecasting
    • Liao K.P., Fildes R. The accuracy of a procedural approach to specifying feedforward neural networks for forecasting. Computers & Operations Research 2005, 32(8):2151-2169.
    • (2005) Computers & Operations Research , vol.32 , Issue.8 , pp. 2151-2169
    • Liao, K.P.1    Fildes, R.2
  • 19
    • 0000032342 scopus 로고    scopus 로고
    • How effective are neural networks at forecasting and prediction? A review and evaluation
    • Adya M., Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting 1998, 17(5-6):481-495.
    • (1998) Journal of Forecasting , vol.17 , Issue.5-6 , pp. 481-495
    • Adya, M.1    Collopy, F.2
  • 20
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi R., John G.H. Wrappers for feature subset selection. Artificial Intelligence 1997, 97(1-2):273-324.
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 21
    • 0034288854 scopus 로고    scopus 로고
    • Automatic neural network modeling for univariate time series
    • Balkin S.D., Ord J.K. Automatic neural network modeling for univariate time series. International Journal of Forecasting 2000, 16(4):509-515.
    • (2000) International Journal of Forecasting , vol.16 , Issue.4 , pp. 509-515
    • Balkin, S.D.1    Ord, J.K.2
  • 24
    • 34548170754 scopus 로고    scopus 로고
    • Methodology for long-term prediction of time series
    • Sorjamaa A., Hao J., Reyhani N., et al. Methodology for long-term prediction of time series. Neurocomputing 2007, 70(16-18):2861-2869.
    • (2007) Neurocomputing , vol.70 , Issue.16-18 , pp. 2861-2869
    • Sorjamaa, A.1    Hao, J.2    Reyhani, N.3
  • 25
    • 56349083799 scopus 로고    scopus 로고
    • Et al., Long-term prediction of time series using NNE-based projection and OP-ELM, in: 2008 IEEE International Joint Conference on Neural Networks.
    • A. Sorjamaa, Y. Miche, R. Weiss, et al., Long-term prediction of time series using NNE-based projection and OP-ELM, in: 2008 IEEE International Joint Conference on Neural Networks, vols. 1-8, 2008, pp. 2674-2680.
    • (2008) , pp. 2674-2680
    • Sorjamaa, A.1    Miche, Y.2    Weiss, R.3
  • 26
    • 0033365782 scopus 로고    scopus 로고
    • Neural model identification, variable selection and model adequacy
    • Refenes A.P.N., Zapranis A.D. Neural model identification, variable selection and model adequacy. Journal of Forecasting 1999, 18(5):299-332.
    • (1999) Journal of Forecasting , vol.18 , Issue.5 , pp. 299-332
    • Refenes, A.P.N.1    Zapranis, A.D.2
  • 27
    • 31644446914 scopus 로고    scopus 로고
    • Building neural network models for time series: a statistical approach
    • Medeiros M.C., Terasvirta T., Rech G. Building neural network models for time series: a statistical approach. Journal of Forecasting 2006, 25(1):49-75.
    • (2006) Journal of Forecasting , vol.25 , Issue.1 , pp. 49-75
    • Medeiros, M.C.1    Terasvirta, T.2    Rech, G.3
  • 28
    • 33644686999 scopus 로고    scopus 로고
    • Optimization-based feature selection with adaptive instance sampling
    • Yang J.Y., Olafsson S. Optimization-based feature selection with adaptive instance sampling. Computers & Operations Research 2006, 33(11):3088-3106.
    • (2006) Computers & Operations Research , vol.33 , Issue.11 , pp. 3088-3106
    • Yang, J.Y.1    Olafsson, S.2
  • 29
    • 1442307850 scopus 로고    scopus 로고
    • Evaluating feature selection methods for learning in data mining applications
    • Piramuthu S. Evaluating feature selection methods for learning in data mining applications. European Journal of Operational Research 2004, 156(2):483-494.
    • (2004) European Journal of Operational Research , vol.156 , Issue.2 , pp. 483-494
    • Piramuthu, S.1
  • 31
    • 14944368253 scopus 로고    scopus 로고
    • Customer targeting: a neural network approach guided by genetic algorithms
    • Kim Y.S., Street W.N., Russell G.J., et al. Customer targeting: a neural network approach guided by genetic algorithms. Management Science 2005, 51:264.
    • (2005) Management Science , vol.51 , pp. 264
    • Kim, Y.S.1    Street, W.N.2    Russell, G.J.3
  • 35
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: the state of the art
    • Zhang G., Patuwo B.E., Hu M.Y. 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.E.2    Hu, M.Y.3
  • 36
  • 37
    • 0000393458 scopus 로고
    • Feed-forward neural nets as models for time series forecasting
    • Tang Z.Y., Fishwick P.A. Feed-forward neural nets as models for time series forecasting. ORSA Journal on Computing 1993, 5(4):374-386.
    • (1993) ORSA Journal on Computing , vol.5 , Issue.4 , pp. 374-386
    • Tang, Z.Y.1    Fishwick, P.A.2
  • 39
    • 0032367976 scopus 로고    scopus 로고
    • Data mining: statistics and more?
    • Hand D.J. Data mining: statistics and more?. American Statistician 1998, 52(2):112-118.
    • (1998) American Statistician , vol.52 , Issue.2 , pp. 112-118
    • Hand, D.J.1
  • 40
    • 0031521391 scopus 로고    scopus 로고
    • Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models
    • Swanson N.R., White H. Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. International Journal of Forecasting 1997, 13(4):439-461.
    • (1997) International Journal of Forecasting , vol.13 , Issue.4 , pp. 439-461
    • Swanson, N.R.1    White, H.2
  • 41
    • 0001606625 scopus 로고    scopus 로고
    • Economic factors and the stock market: a new perspective
    • Qi M., Maddala G.S. Economic factors and the stock market: a new perspective. Journal of Forecasting 1999, 18(3):151-166.
    • (1999) Journal of Forecasting , vol.18 , Issue.3 , pp. 151-166
    • Qi, M.1    Maddala, G.S.2
  • 42
    • 1842710025 scopus 로고    scopus 로고
    • Flexible regression models and relative forecast performance
    • Dahl C.M., Hylleberg S. Flexible regression models and relative forecast performance. International Journal of Forecasting 2004, 20(2):201-217.
    • (2004) International Journal of Forecasting , vol.20 , Issue.2 , pp. 201-217
    • Dahl, C.M.1    Hylleberg, S.2
  • 43
    • 0019636227 scopus 로고
    • Spectrum analysis - a modern perspective
    • Kay S.M., Marple S.L. Spectrum analysis - a modern perspective. Proceedings of the IEEE 1981, 69(11):1380-1419.
    • (1981) Proceedings of the IEEE , vol.69 , Issue.11 , pp. 1380-1419
    • Kay, S.M.1    Marple, S.L.2
  • 46
    • 77952553522 scopus 로고    scopus 로고
    • Forecasting seasonal time series with multilayer perceptrons - an empirical evaluation of input vector specifications for deterministic seasonality.
    • S. Crone, N. Kourentzes, Forecasting seasonal time series with multilayer perceptrons - an empirical evaluation of input vector specifications for deterministic seasonality, pp. 232-238.
    • Crone, S.1    Kourentzes, N.2
  • 47
    • 0009589301 scopus 로고    scopus 로고
    • How to train neural networks
    • Springer, Berlin, New York, G. Orr, K.-R. Müller (Eds.)
    • Neuneier R., Zimmermann H.-G. How to train neural networks. Neural Networks: Tricks of the Trade 1998, 373-423. Springer, Berlin, New York. G. Orr, K.-R. Müller (Eds.).
    • (1998) Neural Networks: Tricks of the Trade , pp. 373-423
    • Neuneier, R.1    Zimmermann, H.-G.2
  • 48
    • 0000902316 scopus 로고    scopus 로고
    • Time series forecasting using neural networks: should the data be deseasonalized first?
    • Nelson M., Hill T., Remus W., et al. Time series forecasting using neural networks: should the data be deseasonalized first?. Journal of Forecasting 1999, 18(5):359-367.
    • (1999) Journal of Forecasting , vol.18 , Issue.5 , pp. 359-367
    • Nelson, M.1    Hill, T.2    Remus, W.3
  • 50
    • 77952549906 scopus 로고    scopus 로고
    • The impact of Data Preprocessing on Support Vector Regression and Artificial Neural Networks in Time Series Forecasting.
    • S.F. Crone, J. Guajardo, R. Weber, The impact of Data Preprocessing on Support Vector Regression and Artificial Neural Networks in Time Series Forecasting.
    • Crone, S.F.1    Guajardo, J.2    Weber, R.3
  • 51
    • 77952541174 scopus 로고    scopus 로고
    • A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns.
    • S.F. Crone, J. Guajardo, R. Weber, A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns.
    • Crone, S.F.1    Guajardo, J.2    Weber, R.3
  • 52
    • 77952541023 scopus 로고    scopus 로고
    • An empirical Evaluation of Support Vector Regression versus Artificial Neural Networks to Forecast basic Time Series Patterns.
    • S.F. Crone, S. Lessmann, S. Pietsch, An empirical Evaluation of Support Vector Regression versus Artificial Neural Networks to Forecast basic Time Series Patterns.
    • Crone, S.F.1    Lessmann, S.2    Pietsch, S.3
  • 53
    • 22444452861 scopus 로고    scopus 로고
    • Extracting information from mega-panels and high-frequency data
    • Granger C.W.J. Extracting information from mega-panels and high-frequency data. Statistica Neerlandica 1998, 52(3):258-272.
    • (1998) Statistica Neerlandica , vol.52 , Issue.3 , pp. 258-272
    • Granger, C.W.J.1
  • 54
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2(5):359-366.
    • (1989) Neural Networks , vol.2 , Issue.5 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 55
    • 77952547695 scopus 로고    scopus 로고
    • Input variable selection for time series prediction with neural networks - an evaluation of visual, autocorrelation and spectral analysis for varying seasonality.
    • S.F. Crone, N. Kourentzes, Input variable selection for time series prediction with neural networks - an evaluation of visual, autocorrelation and spectral analysis for varying seasonality, pp. 195-205.
    • Crone, S.F.1    Kourentzes, N.2
  • 56
    • 4344586989 scopus 로고    scopus 로고
    • Linear versus neural network forecasts for European industrial production series
    • Heravi S., Osborn D.R., Birchenhall C.R. Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting 2004, 20(3):435-446.
    • (2004) International Journal of Forecasting , vol.20 , Issue.3 , pp. 435-446
    • Heravi, S.1    Osborn, D.R.2    Birchenhall, C.R.3
  • 57
    • 0035315158 scopus 로고    scopus 로고
    • Automatic identification of time series features for rule-based forecasting
    • Adya M., Collopy F., Armstrong J.S., et al. Automatic identification of time series features for rule-based forecasting. International Journal of Forecasting 2001, 17(2):143-157.
    • (2001) International Journal of Forecasting , vol.17 , Issue.2 , pp. 143-157
    • Adya, M.1    Collopy, F.2    Armstrong, J.S.3
  • 58
    • 0002847043 scopus 로고
    • The evaluation of extrapolative forecasting methods
    • Fildes R. The evaluation of extrapolative forecasting methods. International Journal of Forecasting 1992, 8(1):81-98.
    • (1992) International Journal of Forecasting , vol.8 , Issue.1 , pp. 81-98
    • Fildes, R.1
  • 59
    • 0034288853 scopus 로고    scopus 로고
    • Out-of-sample tests of forecasting accuracy: an analysis and review
    • Tashman L.J. Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting 2000, 16(4):437-450.
    • (2000) International Journal of Forecasting , vol.16 , Issue.4 , pp. 437-450
    • Tashman, L.J.1
  • 60
    • 0034288853 scopus 로고    scopus 로고
    • Out-of-sample tests of forecasting accuracy: an analysis and review
    • Tashman L. Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting 2000, 16:437-450.
    • (2000) International Journal of Forecasting , vol.16 , pp. 437-450
    • Tashman, L.1
  • 61
    • 0002006114 scopus 로고
    • Error measures for generalizing about forecasting methods: empirical comparisons
    • Armstrong J.S., Collopy F. Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting 1992, 8(1):69-80.
    • (1992) International Journal of Forecasting , vol.8 , Issue.1 , pp. 69-80
    • Armstrong, J.S.1    Collopy, F.2
  • 63
    • 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


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.