Long-Term Time Series Prediction Using k-NN Based LS-SVM Framework with Multi-Value Integration

  • Zifang HuangEmail author
  • Mei-Ling Shyu


Time series modeling and prediction are very attractive topics, which play an important role in many fields such as transportation prediction [4], power prediction [13, 18], and health care study [7]. The purpose of time series prediction is to forecast the values of data points ahead of time, where long-term time series prediction is to make the predictions multi-step ahead. The prediction process is commonly performed by observing and modeling the past values, and assuming that the future values will follow the same trend. When the prediction horizon increases, the uncertainty of the future trend also increases, rendering a more challenging prediction problem. Researchers have dedicated their effort to study how to extract as much knowledge as possible from the past values, and how to better utilize such knowledge for long-term time series prediction. There has been previous research work in order to tackle this challenge based on some classical time series prediction approaches, such as exponential smoothing [12], linear regression [14], autoregressive integrated moving average (ARIMA) [33], support vector machines (SVM) [25], artificial neural networks (ANN) [10, 33], and fuzzy logic [10].


Root Mean Square Error Training Dataset Testing Instance Little Square Support Vector Machine Radial Basis Function Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bontempi, G.: Long term time series prediction with multi-input multi-output local learning. In: 2nd European Symposium on Time Series Prediction pp. 145–154 (2008)Google Scholar
  2. 2.
    Clement, M.P., Hendry, D.F.: Forecasting economic times series. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  3. 3.
    Cristi, R., Tummala, M.: Multirate, multiresolution, recursive kalman filter. Signal Process. 80(9), 1945–1958 (2000)zbMATHCrossRefGoogle Scholar
  4. 4.
    Crone, S.F.: Artificial neural network & computational intelligence forecasting competition, (2010)
  5. 5.
    Farooq, T., Guergachi, A., Krishnan, S.: Chaotic time series prediction using knowledge based green’s kernel and least-squares support vector machines. In: IEEE International Conference on Systems, Man and Cybernetics pp. 373–378 (2007)Google Scholar
  6. 6.
    Herrera, L.J., Pomares, H., Rojas, I., Guilln, A., Prieto, A., Valenzuela, O.: Recursive prediction for long term time series forecasting using advanced models. Neurocomputing 70(16–18), 2870–2880 (2007)CrossRefGoogle Scholar
  7. 7.
    Homma, N., Sakai, M., Takai, Y.: Time series prediction of respiratory motion for lung tumor tracking radiation therapy. In: 10th WSEAS international conference on Neural networks pp. 126–131 (2009)Google Scholar
  8. 8.
    Huang, Z., Shyu, M.L.: k-NN based LS-SVM framework for long-term time series prediction. In: 2010 IEEE International Conference on Information Reuse and Integration pp. 69–74 (2010)Google Scholar
  9. 9.
    Huebner, U., Abraham, N.B., Weiss, C.O.: Dimensions and entropies of chaotic intensity pulsations in a single-mode far-infrared NH 3 laser. Phys. Rev. A 40(11), 6354–6365 (1989)CrossRefGoogle Scholar
  10. 10.
    Jang, J.S.: Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. Syst. Hum. 23(3), 665–685 (1993)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jiang, T., Wang, S., Wei, R.: Support vector machine with composite kernels for time series prediction. In: 4th international symposium on Neural Networks pp. 350–356 (2007)Google Scholar
  12. 12.
    Jones, R.H.: Exponential smoothing for multivariate time series. J. Roy. Stat. Soc. B 28(1), 241–251 (1966)zbMATHGoogle Scholar
  13. 13.
    Kusiak, A., Zheng, H., Song, Z.: Short-term prediction of wind farm power: A data mining approach. IEEE Trans. Energ. Convers. 24(1), 125–136 (2009)CrossRefGoogle Scholar
  14. 14.
    Lin, K., Lin, Q., Zhou, C., Yao, J.: Time series prediction based on linear regression and SVR. Third International Conference on Natural Computation 1, 688–691 (2007)CrossRefGoogle Scholar
  15. 15.
    Liu, P., Yao, J.: Application of least square support vector machine based on particle swarm optimization to chaotic time series prediction. In: IEEE International Conference on Intelligent Computing and Intelligent Systems 4, 458–462 (2009)Google Scholar
  16. 16.
    Lotric, U.: Wavelet based denoising integrated into multilayered perceptron. Neurocomputing 62, 179–196 (2004)CrossRefGoogle Scholar
  17. 17.
    Mackey, M., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)CrossRefGoogle Scholar
  18. 18.
    Maralloo, M., Koushki, A., Lucas, C., Kalhor, A.: Long term electrical load forecasting via a neurofuzzy model. In: 14th International CSI Computer Conference pp. 35–40 (2009)Google Scholar
  19. 19.
    Menezes Jr, J.M.P., Barreto, G.A.: Long-term time series prediction with the narx network: An empirical evaluation. Neurocomputing 71(16-18), 3335–3343 (2008)CrossRefGoogle Scholar
  20. 20.
    Meng, K., Dong, Z., Wong, K.: Self-adaptive radial basis function neural network for short-term electricity price forecasting. IET Gener. Transm. Distrib. 3(4), 325–335 (2009)CrossRefGoogle Scholar
  21. 21.
    Nguyen, H.H., Chan, C.W.: Multiple neural networks for a long term time series forecast. Neural. Comput. Appl. 13, 90–98 (2004)CrossRefGoogle Scholar
  22. 22.
    Pelckmans, K., Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Lukas, L., Moor, B.D., Vandewalle, J.: LS-SVMlab toolbox user’s guide. ESAT-SCD-SISTA Technical Report pp. 1–106 (2003)Google Scholar
  23. 23.
    Puma-Villanueva, W.J., dos Santos, E., Von Zuben, F.: Long-term time series prediction using wrappers for variable selection and clustering for data partition. In: International Joint Conference on Neural Networks pp. 3068–3073 (2007)Google Scholar
  24. 24.
    Renaud, O., Starck, J.L., Murtagh, F.: Wavelet-based combined signal filtering and prediction. IEEE Trans. Syst. Man Cybern. B Cybern. 35(6), 1241–1251 (2005)CrossRefGoogle Scholar
  25. 25.
    Sapankevych, N., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009)CrossRefGoogle Scholar
  26. 26.
    Sfetsos, A., Siriopoulos, C.: Time series forecasting with a hybrid clustering scheme and pattern recognition. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 34(3), 399–405 (2004)CrossRefGoogle Scholar
  27. 27.
    Soltani, S., Boichu, D., Simard, P., Canu, S.: The long-term memory prediction by multiscale decomposition. Signal Process. 80(10), 2195–2205 (2000)zbMATHCrossRefGoogle Scholar
  28. 28.
    Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70(16-18), 2861–2869 (2007)CrossRefGoogle Scholar
  29. 29.
    Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Farrer Road, Singapore (2002)zbMATHCrossRefGoogle Scholar
  30. 30.
    Taieb, S.B., Bontempi, G., Sorjamaa, A., Lendasse, A.: Long-term prediction of time series by combining direct and mimo strategies. In: International Joint Conference on Neural Networks pp. 3054–3061 (2009)Google Scholar
  31. 31.
    Wei, H.L., Billings, S.A.: Long term prediction of non-linear time series using multiresolution wavelet models. Int. J. Contr. 79(6), 569–580 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Yegnanarayana, B.: Artificial Neural Networks. Prentice-Hall of India Pvt. Ltd, New Delhi (2004)Google Scholar
  33. 33.
    Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)zbMATHCrossRefGoogle Scholar
  34. 34.
    Zou, H., Yang, Y.: Combining time series models for forecasting. Int. J. Forecast. 20(1), 69–84 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Vienna 2012

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of MiamiCoral GablesUSA

Personalised recommendations