A Fusion Financial Prediction Strategy Based on RNN and Representative Pattern Discovery

Abstract

To predicate the future with high accuracy is a holy grail in financial market. However, the volatility of chaotic financial market challenges new technologies from computer science to economic science all the time. Recently, Recurrent Neural Network (RNN) plays a new role in financial market prediction. However, results from RNN are restricted by sample size of training datasets, and show predication accuracy can hardly be guaranteed in a long term. On the other hand, Representative Pattern Discovery (RPD) is an effective way in long-term prediction while it is ineffective in short-term prediction. In this paper, we define a representative pattern for time series, and propose a fusion financial prediction strategy based on RNN and RPD. We take the advantages of both RNN and RPD, in the way that the proposed strategy is stateful to keep the short-term trend and it rectifies the predication by a time-dependent incremental factor in a long-term way. Compared with RNN and pattern discovery respectively, our experimental results demonstrate that our proposed strategy performs much better than that of others. It can increase the prediction accuracy by 6% on the basis of RNN at most, but at a cost of higher Mean Squared Error.