Deep Learning-Based Feature Fusion and Prediction Approach For Stock Market Forecasting
學年 114
學期 1
出版（發表）日期 2025-10-01
作品名稱 Deep Learning-Based Feature Fusion and Prediction Approach For Stock Market Forecasting
作品名稱（其他語言）
著者 Tzu-Chia Chen
單位
出版者
著錄名稱、卷期、頁數 Applied Soft Computing(182), p.113623
摘要 Generally, stock market prediction is a current renowned research topic. The existing prediction approaches are on the basis of the econometric and statistical approaches. Nevertheless, these approaches are complex to pact with non-stationary time series data. Thus, this study develops a new approach for stock market prediction utilizing a hybrid deep learning approach. In this work, the pre-processing stage is done initially with the assistance of yeo-jhonson transformation and padding-based Fourier transform (FT) denoising model. After that, technical indicators, like Williams’s %R, Rate of Change, Triple Exponential Moving Average (TRIX), Average Directional Index (ADX), Average True Range (ATR), and Relative Strength Index (RSI), are extracted. Subsequently, the feature fusion procedure is done by utilizing Morisita's overlap index and Deep Belief Network (DBN) model. Lastly, stock market forecasting is done by a hybrid approach integrating Deep Long Short-Term Memory (Deep LSTM) and Multi-Layer Perceptron (MLP). Moreover, the proposed model is compared with the conventional models, such as Padding-based Fourier Transform Denoising (P-FTD)+Recurrent Neural Network (RNN), Feedforward Neural Network (FNN)+Back-propagation Neural Network (BPNN), Residual-CNN-Seq2Seq (RCSNet), Long short-term memory (LSTM), Competitive Feedback Particle Swarm Optimization-based Deep Recurrent Neural Network (CFPSO-based Deep RNN) and Rider Deep LSTM & Deep RNN. Finally, the experimentation analysis states that the performance of Deep LSTM-MLP is superior to conventional approaches regarding the MSE, RMSE and MAE with the values of 0.113, 0.337, and 0.169.
關鍵字 Stock market; Prediction; Feature fusion; Deep learning; Pre-processing; Technical indicators
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127745 )

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