Internet Finance Non-stationary Time Series Prediction Algorithm Based on Deep Learning

Abstract

Inaccurate prediction results of financial time series will lead to wrong investment decisions. Therefore, a prediction algorithm for Internet financial non-stationary time series based on deep learning is proposed. EMD (empirical mode decomposition) method is used to divide the collected historical Internet financial non-stationary time series information into high-frequency and low-frequency parts, and remove the noise in the decomposed high-frequency components to obtain the financial non-stationary time series without noise. The knowledge map method is used to mine the transaction characteristics and market characteristics of Internet finance from the financial non-stationary time series without noise, and the two are fused as the input of the improved CNN (convolutional neural network) prediction model. The prediction results of Internet financial time series are obtained through CNN. The experimental results show that after setting the CNN parameters, the predicted results are consistent with the actual market trends. The highest RSE of the predicted result is 0.551, The highest RAE is 0.443, which is relatively low, the CORR value is 0.864, which is relatively high, indicating that the relative square root error, relative absolute error, and relevant empirical coefficients of the prediction results are all good, making it a highly applicable algorithm.