基于分形插值与机器学习模型的股指分析和预测

股票市场预测一直是金融市场分析中的热点和难点,一些传统的预测模型很难对股票市场做出有效的预测;针对这一问题,将分形插值方法与机器学习算法相结合,提出了分形插值与SVM以及分形插值与BP神经网络两种混合模型;所提的混合模型利用机器学习算法首先计算出分形插值所需要的插值点,然后建立分形插值外推模型对所需其他值进行预测;实证结果发现两个混合模型的预测效果均比单独使用分形插值模型预测效果更佳,预测精度更高;因此分形插值方法与机器学习算法相结合所得到的混合模型,能较好地预测诸如股票市场指数等非平稳金融时间序列。

Analysis and Forecast of Stock Indexes Based on Fractal Interpolation and Machine Learning Models

Stock market forecast is always a hot and difficult point in financial market analysis.Some traditional forecast models are difficult to predict the stock market effectively.In order to solve this problem, two hybrid models of fractal interpolation and SVM, as well as fractal interpolation and BP neural network are proposed by combining fractal interpolation method with machine learning algorithm.The proposed hybrid models firstly use machine learning algorithm to calculate the interpolation points needed for fractal interpolation, and then build a fractal interpolation extrapolation model to predict other values.The empirical results show that the prediction effect of the two hybrid models is better than that of the fractal interpolation model alone, and the prediction accuracy is higher.Therefore, the hybrid model obtained by the combination of fractal interpolation method and machine learning algorithm can better predict non-stationary financial time series such as stock market index.