Abstract
The echo state network (ESN) is a dynamic neural network, which simplifies the training process in the conventional neural network. Due to its powerful non-linear computing ability, it has been applied to predict the time series. However, the parameters of the ESN need to be set experimentally, which can lead to instable performance and there is space to further improve its performance. In order to address this challenge, an improved fruit fly optimization algorithm (IFOA) is proposed in this work to optimize four key parameters of the ESN. Compared to the original fruit fly optimization algorithm (FOA), the proposed IFOA improves the optimization efficiency, where two novel particles are proposed in the fruit flies swarm, and the search process of the swarm is transformed from two-dimensional to three-dimensional space. The proposed approach is applied to financial data sets. Experimental results show that the proposed FOA-ESN and IFOA-ESN models are more effective (~50% improvement) than others, and the IFOA-ESN can obtain the best prediction accuracy.
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Acknowledgement
This research is supported by the National Natural Science Foundation of China under Grant 61603104, the Guangxi Natural Science Foundation under Grants 2016GXNSFCA380017, 2015GXNSFBA139256 and 2017GXNSFAA198180, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Doctoral Research Foundation of Guangxi Normal University under Grant 2016BQ005, the Scientific Research Funds for the Returned Overseas Chinese Scholars from State Education Ministry, the Funds for Young Key Program of Education Department from Fujian Province, China (Grant No. JZ160425), and Program of Education Department of Fujian Province, China (Grant No. I201501005).
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Liu, J. et al. (2018). Financial Data Forecasting Using Optimized Echo State Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_13
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