Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)

Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression. The key point of the phase space reconstruction is to choose the optimal delay time, and to find the optimal embedding dimension of space. This paper proposes the use of false nearest neighbor method to construct the error function for all the variables to determine the appropriate embedding dimension combinations. Kernel function in the SVR is an important factor for algorithm performance. Experiments show that the theory of phase space reconstruction based on support vector regression has a certain degree of predictive ability of market value at risk.