Analysis of Recurrent Neural Networks for Henon Simulated Time-Series Forecasting
Guarin, Diego L., and Robert E. Kearney. "Identification of a TimeVarying, Box-Jenkins Model of Intrinsic Joint Compliance." IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016.
Chen, Shangyuan, Jinfeng Mao, and Xu Han. "Heat transfer analysis of a vertical ground heat exchanger using numerical simulation and multiple regression model." Energy and Buildings 129:81-91, 2016.
Levine, D. M. Basic business statistics. Pearson Australia Group, 2013.
Taylor, J.W., “Short-term load forecasting with exponentially weighted methods”. IEEE Transactions on Power Systems, 27(1):458–464, 2012.
Y., M., “A fuzzy logic fog forecasting model for perth airport”. Pure and Applied Geophysics, 169(5-6):1107–1119, 2012.
Egrioglu, “Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks”. Expert Systems with Applications, 40(3):854–857, 2013
Shen, Xin, et al. "Support Vector Machine Classifier with Truncated Pinball Loss." Pattern Recognition, 2017.
Oscar and Torra, S., “Forecasting tourism demand to catalonia: Neural networks vs. time series models”. Economic Modelling, 36:220–228, 2014
Xue, “A novel hybrid approach for wind power forecasting”. In Unifying Electrical Engineering and Electronics Engineering, pages 1019–1027. Springer, 2014
Liu, Y. and Jiang, “An optimal atm cash replenishment solution using ann-based bagging algorithm”. In Proceedings of the 9th International Symposium on Linear Drives for Industry Applications, Volume 1, pages 217–224. Springer, 2014
Menezes Jr, J. M. P. and Barreto, G. A., “Long-term time series prediction with the narx network: An empirical evaluation”. Neurocomputing, 71(16):3335–3343, 2008
Méndez, Eduardo, Omar Lugo, and Patricia Melin. "A Competitive Modular Neural Network for Long-Term Time Series Forecasting." Nature-Inspired Design of Hybrid Intelligent Systems. Springer International Publishing, 243-254, 2017.
Fink, O., Zio, E., and Weidmann, U., “Predicting component reliability and level of degradation with complex-valued neural networks”. Reliability Engineering & System Safety, 121:198–206, 2014
Haykin, S. S., “Neural networks and learning machines”, volume 3. Pearson Education Upper Saddle River, 2009
Ardalani-Farsa, M., “Chaotic time series prediction with residual analysis method using hybrid elman–narx neural networks”. Neurocomputing, 73(13):2540–2553, 2011
Pascanu, R., Mikolov, T., and Bengio, Y., “On the difficulty of training recurrent neural networks”. arXiv preprint., 2012
Bengio, Y., Boulanger-Lewandowski, N., and Pascanu, R., “Advances in optimizing recurrent networks”. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8624–8628. IEEE, 2013
Abdulkadir, S. J., & Yong, S. P., “Scaled UKF–NARX hybrid model for multi-step-ahead forecasting of chaotic time series data”. Soft Computing, 19(12), 3479-3496, 2015
Abdulkadir, S. J., Yong, S.-P., Marimuthu, M., and Lai, F.-W., “Hybridization of unscented kalman filter and non-linear autoregressive neural network for financial forecasting”. In Mining Intelligence and Knowledge Exploration, pages 72–81, 2014
Ma, Q.-L., Zheng, Q.-L., Peng, H., Zhong, T.-W., and Xu, L.-Q., “Chaotic time series prediction based on evolving recurrent neural networks”. In International Conference on Machine Learning and Cybernetics, volume 6, pages 3496–3500. IEEE, 2007
Abdulkadir, S. J. and Yong, S.-P., “Unscented kalman filter for noisy multivariate financial time-series data”. In Multi-disciplinary Trends in Artificial Intelligence, pages 87–96. Springer, 2013
Sivakumar, Bellie. "Modern Nonlinear Time Series Methods." Chaos in Hydrology. Springer Netherlands, 111-145, 2017.
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