Abstract
In this paper, the mixture of Gaussian processes (MGP) is applied to model and predict the time series of stock prices. Methodically, the precise hard-cut expectation maximization (EM) algorithm for MGPs is utilized to learn the parameters of the MGP model from stock prices data. It is demonstrated by the experiments that the MGP model with the precise hard-cut EM algorithm can be successfully applied to the prediction of stock prices, and outperforms the typical regression models and algorithms.
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Acknowledgement
This work was supported by the Natural Science Foundation of China for Grant 61171138.
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Liu, S., Ma, J. (2016). Stock Price Prediction Through the Mixture of Gaussian Processes via the Precise Hard-cut EM Algorithm. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_27
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DOI: https://doi.org/10.1007/978-3-319-42297-8_27
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