金融理论与教学2024，Vol.42Issue(4)：1-8,8.
基于AFD-LSTM模型的金融信号去噪与预测
Denoising and Prediction of Financial Signals Based on AFD-LSTM Model
摘要
Abstract
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.关键词
AFD-LSTM模型/金融信号/预测Key words
AFD-LSTM model/financial signal/prediction分类
管理科学引用本文复制引用
王晋勋,麦骏希..基于AFD-LSTM模型的金融信号去噪与预测[J].金融理论与教学,2024,42(4):1-8,8.基金项目
国家自然科学基金项目"超复解析核函数及其在自适应Fourier分解中的应用"(11701105). （11701105）
