Abstract
Large computational quantity and cumulative error are main shortcomings of add- weighted one-rank local-region single-step method for multi-steps prediction of chaotic time series. A local-region multi-steps forecasting model based on phase-space reconstruction is presented for chaotic time series prediction, including add-weighted one-rank local-region multi-steps forecasting model and RBF neural network multi-steps forecasting model. Simulation results from several typical chaotic time series demonstrate that both of these models are effective for multi-steps prediction of chaotic time series.
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References
Li, T., Liu, Z.: The chaotic property of power load and its forecasting. Proceedings of the CSEE 20(11), 36–40 (2000)
Lü, J., Zhang, S.: Application of adding-weight one-rank local region method in electric power system short-term load forecast. Control theory and applications 19(5), 767–770 (2002)
Liu, H., Qi, H., Cai, Z.: Nonlinear chaotic model of landslide forecasting. Chinese Journal of Rock Mechanics and Engineering 22(3), 434–437 (2003)
Gu, Y., Chen, T., Huang, W.: Forecasting of Heng Sheng index futures based on neural networks based on chaotic algorithm. Acta Scientiarum Naturalium Universitatis Nankaiensis 33(1), 119–123 (2000)
Lü, J., Lu, J., Chen, S.: Chaotic time series analysis and its applications. Wuhan University Publication, Wuhan (2002)
Grassberger, P., Procaccia, I.: Characterization of strange attractors. Phys. Rev. Lett. 50, 346 (1983)
Yang, S., Jia, C.: Two practical methods of phase space reconstruction. ACTA Physica Sinica 51(11), 2452–2458 (2002)
Lu, J., Lü, J., Chen, S.: Chen’s chaotic attractor and its characteristic quantity. Control theory and applications 19(2), 308–310 (2002)
Liu, M., Hu, Z.: Chaos prediction of hydrodynamic behavior in gas-liquid two-phase bubble column with single orifice. Journal of Chemical Industry and Engineering 51(4), 475–479 (2000)
Jiang, Y., Shen, J., Yang, B.: A method of chaotic time series prediction based on wavelet neural network. Microcomputer development 37(3), 37–39 (2001)
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© 2004 Springer-Verlag Berlin Heidelberg
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Cai, M., Cai, F., Shi, A., Zhou, B., Zhang, Y. (2004). Chaotic Time Series Prediction Based on Local-Region Multi-steps Forecasting Model. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_66
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DOI: https://doi.org/10.1007/978-3-540-28648-6_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
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