Denoising and Prediction of Financial Signals Based on AFD-LSTM Model
Aiming to improve the accuracy of data prediction of financial time series,the paper proposes a prediction model that combines the Adaptive Fourier Decomposition(AFD)method with the Long Short-Term Memory(LSTM)neural network model.AFD is a signal processing method,which has the adaptability that Fourier transform does not have.It can quickly extract the characteristics of financial signals and achieve the purpose of removing signal noise pollution.The LSTM model can explore the dependency relationships of time series data,which is very effective for predicting financial time series data with long memory.Based on AFD-LSTM model,the research conducts the empirical analysis on four kinds of financial signal data,namely,USD/RMB exchange rate,SZSE 700 stock index,current price of gold in London and Guangdong Province carbon emission quota(GDEA)price and compares them with the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The results show that the LSTM network model trained based on the financial data denoising by the AFD method has high predictive accuracy and does not depend on the layer number parameters of the LSTM model and has strong stability.
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