Time series forecasting based on a multidimensional Taylor network model with clustering of dynamic characteristics

References

• Annakkage, U. D., Nair, N. K. C., Liang, Y. F., Gole, A. M., Dinavahi, V., Gustavsen, B., Noda, T., Ghasemi, H., Monti, A., Matar, M., Iravani, R., & Martinez, J. A. (2012). Dynamic system equivalents: A survey of available techniques. IEEE Transactions on Power Delivery, 27(1), 411–420. https://doi.org/10.1109/TPWRD.2011.2167351
   Web of Science ®Google Scholar
• Aytac, B., & Wu, S. D. (2011). Modelling high-tech product life cycles with short-term demand information: A case study. Journal of the Operational Research Society, 62(3), 425–432. https://doi.org/10.1057/jors.2010.89
   Web of Science ®Google Scholar
• Barlas, Y., & Gunduz, B. (2011). Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society, 62(3), 458–473. https://doi.org/10.1057/jors.2010.188
   Web of Science ®Google Scholar
• Bermudez, J. D., Segura, J. V., & Vercher, E. (2010). Bayesian forecasting with the Holt-Winters model. Journal of the Operational Research Society, 61(1), 164–171. https://doi.org/10.1057/jors.2008.152
   Web of Science ®Google Scholar
• Bodyanskiy, Y., & Popov, S. (2006). Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research, 175(3), 1357–1366. https://doi.org/10.1016/j.ejor.2005.02.012
   Web of Science ®Google Scholar
• Borovykh, A., Bohte, S., & Oosterlee, C. W. (2019). Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance, 22(4), 73–101. 10.21314/JCF.2018.358
   Google Scholar
• Bunn, D. W. (1996). Non-traditional methods of forecasting. European Journal of Operational Research, 92(3), 528–536. https://doi.org/10.1016/0377-2217(96)00006-9
   Web of Science ®Google Scholar
• Cholette, M. E., Liu, J. B., Djurdjanovic, D., & Marko, K. A. (2012). Monitoring of complex systems of interacting dynamic systems. Applied Intelligence, 37(1), 60–79. https://doi.org/10.1007/s10489-011-0313-0
   Web of Science ®Google Scholar
• Cincotti, S., Gallo, G., Ponta, L., & Raberto, M. (2014). Modeling and forecasting of electricity spot-prices: Computational intelligence vs. classical econometrics. AI Communications, 27(3), 301–314. https://doi.org/10.3233/AIC-140599
   Web of Science ®Google Scholar
• Dasgupta, S., & Osogami, T. (2017). Nonlinear dynamic Boltzmann machines for time series prediction (pp. 1833–1839) [Paper presentation]. AAAI, San Francisco, CA.
   Google Scholar
• Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
   Web of Science ®Google Scholar
• Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors. Physical Review Letters, 50(5), 346–349. https://doi.org/10.1103/PhysRevLett.50.346
   Web of Science ®Google Scholar
• Groß-KlußMann, A., & Hautsch, N. (2013). Predicting bid-ask spreads using long-memory autoregressive conditional Poisson models. Journal of Forecasting, 32(8), 724–742. https://doi.org/10.1002/for.2267
   Web of Science ®Google Scholar
• Han, X., Xie, W., Fu, Z., & Luo, W. (2011). Nonlinear systems identification using dynamic multi-time scale neural networks. Neurocomputing, 74(17), 3428–3439. https://doi.org/10.1016/j.neucom.2011.06.007
   Web of Science ®Google Scholar
• He, W. W., Wang, Z. Z., & Jiang, H. (2008). Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing, 72(1–3), 600–611. https://doi.org/10.1016/j.neucom.2007.11.010
   Web of Science ®Google Scholar
• Hsu, L. C., & Wang, C. H. (2009). Forecasting integrated circuit output using multivariate grey model and grey relational analysis. Expert Systems with Applications, 36(2), 1403–1409. https://doi.org/10.1016/j.eswa.2007.11.015
   Web of Science ®Google Scholar
• Johnston, F. R., Shale, E. A., Kapoor, S., True, R., & Sheth, A. (2011). Breadth of range and depth of stock: Forecasting and inventory management at Euro Car Parts Ltd. Journal of the Operational Research Society, 62(3), 433–441. https://doi.org/10.1057/jors.2010.189
   Web of Science ®Google Scholar
• Kadri, F., Harrou, F., Chaabane, S., & Tahon, C. (2014). Time series modelling and forecasting of emergency department overcrowding. Journal of Medical Sysytems, 38(9), 107. https://doi.org/10.1007/s10916-014-0107-0
   PubMed Web of Science ®Google Scholar
• Lin, Y., Yan, H. S., & Zhou, B. (2014). Nonlinear time series prediction method based on multi-dimensional Taylor network and its applications. Control and Decision, 29(5), 795–801. https://doi.org/10.1016/j.ifacol.2018.09.421
   Google Scholar
• Magdon-Ismail, M., & Abu-Mostafa, Y. S. (1998). Validation of volatility models. Journal of Forecasting, 17(5-6), 349–368.
   Google Scholar
• Matias, J. M., & Reboredo, J. C. (2012). Forecasting performance of nonlinear models for intraday stock returns. Journal of Forecasting, 31(2), 172–188. https://doi.org/10.1002/for.1218
   Web of Science ®Google Scholar
• Mittelman, R. (2015). Time-series modeling with undecimated fully convolutional neural networks. arXiv preprint arXiv:1508.00317.
   Google Scholar
• Muhammad, Z., Rozaida, G., Muhammad, G. G., Yana, M. M. H., & Irfan, J. (2020). Predicting financial prices of stock market using recurrent convolutional neural networks. International Journal of Intelligent Systems and Applications, 12(6), 21–32. https://doi.org/10.5815/ijisa.2020.06.02
   Google Scholar
• Naduvil-Vadukootu, S., Angryk, R. A., & Riley, P. (2017). Evaluating preprocessing strategies for time series prediction using deep learning architectures (pp. 520–525). FLAIRS Conference, Marco Island, Florida, USA.
   Google Scholar
• Ourari, M. L., Dessaint, L. A., & Do, V. Q. (2006). Dynamic equivalent modeling of large power systems using structure preservation technique. IEEE Transactions on Power Systems, 21(3), 1284–1295. https://doi.org/10.1109/TPWRS.2006.876699
   Web of Science ®Google Scholar
• Sermpinis, G., Theofilatos, K., Karathanasopoulos, M., Georgopoulos, E. F., & Dunis, C. (2013). Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization. European Journal of Operational Research, 225(3), 528–540. https://doi.org/10.1016/j.ejor.2012.10.020
   Web of Science ®Google Scholar
• Shen, Z. P., Zhang, Y. M., Lu, J. W., Xu, J., & Xiao, G. (2020). A novel time series forecasting model with deep learning. Neurocomputing, 396(5), 302–313. https://doi.org/10.1016/j.neucom.2018.12.084
   Google Scholar
• Sjöberg, J., Zhang, Q., Ljung, L., Benveniste, A., Delyon, B., Glorennec, P. Y., Hjalmarsson, H., & Juditsky, A. (1995). Nonlinear black-box modeling in system identification: A unified overview. Automatica, 31(12), 1691–1724. https://doi.org/10.1016/0005-1098(95)00120-8
   Web of Science ®Google Scholar
• Slowinski, R., Zopounidis, C., & Dimitras, A. I. (1997). Prediction of Company acquisition in Greece by means of the rough set approach. European Journal of Operational Research, 100(1), 1–15. https://doi.org/10.1016/S0377-2217(96)00110-5
   Web of Science ®Google Scholar
• Suzuki, A. K., Salasar, L. E. B., Leite, J. G., & Louzada-Neto, F. (2010). A Bayesian approach for predicting match outcomes: The 2006 (Association) Football World Cup. Journal of the Operational Research Society, 61(10), 1530–1539. https://doi.org/10.1057/jors.2009.127
   Web of Science ®Google Scholar
• Wittkemper, H. G., & Steiner, M. (1996). Using neural networks to forecast the systematic risk of stocks. European Journal of Operational Research, 90(3), 577–588. https://doi.org/10.1016/0377-2217(95)00099-2
   Web of Science ®Google Scholar
• Wong, W. K., Xia, M., & Chu, W. C. (2010). Adaptive neural network model for time-series forecasting. European Journal of Operational Research, 207(2), 807–816. https://doi.org/10.1016/j.ejor.2010.05.022
   Web of Science ®Google Scholar
• Yan, H. S., Han, Y. Q., & Sun, Q. M. (2018). Optimal output-feedback tracking of SISO stochastic nonlinear systems using multidimensional Taylor network. Transactions of the Institute of Measurement and Control, 40(10), 3049–3058. https://doi.org/10.1177/0142331217712380
   Web of Science ®Google Scholar
• Yan, Y. Q., Zhang, H. Q. Q., & Ye, B. H. B. (2014). Assessing the impacts of the high-speed train on tourism demand in China. Tourism Economics, 20(1), 157–169. https://doi.org/10.5367/te.2013.0260
   Web of Science ®Google Scholar
• Zhou, B., & Yan, H. S. (2013). Financial time series forecasting based on wavelet and multidimensional Taylor network dynamics model. System Engineering Theory and Practice, 33(10), 2654–2662. https://doi.org/10.12011/1000-6788(2013)10-2654
   Google Scholar
• Zhu, L., Wang, Y. X., & Fan, Q. B. (2014). MODWT-ARMA model for time series prediction. Applied Mathematical Modelling, 38(5–6), 1859–1865. https://doi.org/10.1016/j.apm.2013.10.002
   Web of Science ®Google Scholar