Data Mining Technology Based Financial Time Series Forecasting Algorithm

摘要

This paper concentrates on the problem of financial time series forecasting, which is of great importance in modern financial management. As is a kind of data mining technology, support vector regression has been widely used in the field of pattern recognition and artificial intelligence. In this paper, we propose an efficient financial time series forecasting algorithm based on a fuzzy SVR model. The financial time series forecasting data are collected from training data by experts, and then the lower bound and upper bound of support vector regression are obtained through fuzzy variables. As the proposed fuzzy SVR based financial time series forecasting depends on the parameter selection greatly, we use the genetic algorithm to obtain optimal parameters. Afterwards, the financial time series can be predicted using the fuzzy SVR model. To testify the effectiveness of our algorithm, a series of experiments are designed and implemented. Experimental results verify that compared with other methods, the proposed can forecast the financial time series with high accuracy and low time consumption.