Stock price prediction through the mixture of Gaussian processes via the precise hard-cut EM algorithm

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.