From Technical Indicators to Trading Decisions: A Deep Learning Model Combining CNN and LSTM

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(6)

Published: Jan. 1, 2024

Stock market prediction is a highly attractive and popular field within finance, driven by the potential for significant profits that come with substantial risks due to data non-linearity complex economic principles. Extracting features from trading crucial in this domain, numerous strategies have been developed. Among these, deep learning has achieved impressive results financial applications because of its robust processing capabilities. In our study, we propose hybrid model, CNN-LSTM, which combines 2D Convolutional Neural Network (CNN) image Long Short-Term Memory (LSTM) network managing sequences classification. We transformed top 15 21 technical indicators time series into 15x15 images different day periods. Each then categorized as Sell, Hold, or Buy based on data. Our model demonstrates superior performance stock predictions over other models.

Language: Английский

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Proposing an innovative model for solar irradiance and wind speed forecasting

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 125224 - 125224

Published: Dec. 1, 2024

Language: Английский

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Palm Oil Production Prediction Using Support Vector Regression Algorithm and Long Short-Term Memory

Published: June 28, 2024

Language: Английский

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Forecasting Volatility of New Energy Stock Prices: An Integrated CEEMDAN and BiLSTM-Transformer Hybrid Model

Published: Dec. 27, 2024

Language: Английский

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