Abstract
It is difficult to predict the opening price of stock accurately, so it is very important to predict the change space of the opening price. Twin support vector regression (TSVR) has good generalization performance in dealing with large-scale data sets, but the selection of its parameters has great blindness and randomness, which has a great impact on its learning performance. In order to solve this problem, an improved whale optimization algorithm is used to optimize the parameters of TSVR. So we propose improved whale optimized twin support vector regression (IWOA-TSVR) in this paper. Finally, IWOA-TSVR used to predict the change space of stock opening price. The experimental results show that the method has high accuracy, good stability and easy operation.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61662005). Guangxi Natural Science Foundation (2018GXNSFAA294068); Basic Ability Improvement Project for Young and Middle-aged Teachers in Colleges and Universities in Guangxi (2019KY0195); Research Project of Guangxi University for Nationalities (2019KJYB006).
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Huang, H., Wei, X., Zhou, Y. (2021). Spatial Prediction of Stock Opening Price Based on Improved Whale Optimized Twin Support Vector Regression. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_57
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DOI: https://doi.org/10.1007/978-3-030-84522-3_57
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