Stock Price Prediction Through the Mixture of Gaussian Processes via the Precise Hard-cut EM Algorithm
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 mode
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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. Keywords: Mixture of Gaussian processes EM algorithm learning Stock price Times series prediction
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1 Introduction The stock market has the characteristics of high return and high risk [1], which has always been concerned on the analysis and forecast of stock prices. Actually, the complexity of the internal structure in stock price system and the diversity of the external factors (the national policy, the bank rate, price index, the performance of quoted companies and the psychological factors of the investors) determine the complexity of the stock market, uncertainty and difficulty of stock price forecasting task [2]. Because the stock price is collected according to the order of time, it actually forms a complex nonlinear time series [3]. Some traditional stock market analysis methods, such as stock price graph analysis (k line graph [4]), cannot profoundly reveal the stock intrinsic relationship, so that the prediction results are not so ideal on stock price. Stock price prediction methodologies fall into three broad categories which are fundamental analysis, technical analysis (charting) and technological methods. From the view of mathematics, the key to effective stock price prediction is to discover the intrinsic mapping or function, and to fit and approximate the mapping or the function. As it has been quickly developed, the mixture of Gaussian processes (MGP) model [5] is a powerful tool for solving this problem. But most of the MGP models are very complex and involve a large number of parameters and hyper-parameters, which makes the application of the MGP models very difficult [6]. Thus, we adopt the MGP model which proposed in [7] with excluding unnecessary priors and carefully selecting the model structure and gating function. This MGP model © Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part III, LNAI 9773, pp. 282–293, 2016. DOI: 10.1007/978-3-319-42297-8_27
Stock Price Prediction Through the Mixture of Gaussian Processes
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remains the main structure, features and advantages of the original MGP model. Moreover, it can be effectively applied to the modeling and prediction of nonlinear time series via the precise hard-cut EM algorithm. In fact, the precise hard-cut EM algorithm is more efficient than the soft EM algorithm since we could get the hyper-parameters of each GP independently in the M-step. It was demonstrated by the experimental results that this precise hard-cut EM algorithm for the MGP model really gives more precise predicti
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