Analysis of Recurrent Neural Networks for Henon Simulated Time-Series Forecasting

Said Jadid Abdulkadir, Hitham Alhussian, Ahmed Ibrahim Alzahrani

Abstract


Forecasting of chaotic time-series has increasingly become a challenging subject. Non-linear models such as recurrent neural networks have been successfully applied in generating short term forecasts, but perform poorly in long term forecasts due to the vanishing gradient problem when the forecasting period increases. This study proposes a robust model that can be applied in long term forecasting of henon chaotic time-series whilst reducing the vanishing gradient problem through enhancing the models ability in learning of long-term dependencies. The proposed hybrid model is tested using henon simulated chaotic time-series data. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the generated forecasts. Performance evaluation results confirm that the proposed recurrent model performs long term forecasts on henon chaotic time-series effectively in terms of error metrics compared to existing forecasting models.

Keywords


Chaotic Time-Series; Recurrent Networks; Henon Time-Series;

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References


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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ISSN: 2180-1843

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