A Fuzzy Time Series Forecasting Model for Financial Data Based on ARIMA
Timing analysis has played a more and more important role in the financial data mining. The incompleteness and uncertainty of historical data restricts the accuracy of the prediction of methods based on the financial time series. Innovatively defines the similarity and mold of the ARIMA model, combined with the fuzzy time series forecasting model, proposes a new model named ARIMA-based fuzzy time series forecasting model. This model can handle the incomplete or vague historical data effectively, and make valid predictions on the future trend. For one thing, the ARIMA model is concise and flexible, which simplify the feature extraction of high dimensional financial time series greatly; on the other side, prediction method based on the fuzzy logic theory can effectively find the similar patterns in time series. Takes the RMB-dollar rate as an example, analyzes the prediction results of our new provided method with traditional methods. The new method proves to be valid and effective.