An Optimized Extreme Learning Machine Composite Framework for Point, Probabilistic, and Quantile Regression Forecasting of Carbon Price

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106772 - 106772

Опубликована: Янв. 1, 2025

Язык: Английский

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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction

Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4315 - 4315

Опубликована: Апрель 14, 2025

Stock price prediction and portfolio optimization are critical research areas in financial markets, as they directly impact investment strategies risk management. Traditional statistical methods machine learning approaches have been widely applied to these tasks, but often fail fully capture the complex dynamics of markets. typically rely on unrealistic assumptions or oversimplified models, neglecting nonlinear high-dimensional characteristics market data. Additionally, deep methods, especially temporal convolution networks graph attention networks, introduced this area achieved significant improvements both stock optimization. Therefore, study proposes a Spatial–Temporal Graph Attention Network (STGAT) that integrates STL decomposition components structures model patterns asset correlations. By combining mechanisms with convolutional modules, STGAT effectively processes spatiotemporal data, enhancing accuracy predictions. Empirical experiments CSI 500 S&P datasets demonstrate outperforms other models performance. The portfolios constructed based STGAT’s predictions achieve higher returns real scenarios, which validates feasibility feature fusion for highlights advantages capturing characteristics. This not only provides robust tool also offers valuable insights future intelligent systems.

Язык: Английский

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Intelligent crude oil price probability forecasting: Deep learning models and industry applications

Computers in Industry, Год журнала: 2024, Номер 163, С. 104150 - 104150

Опубликована: Сен. 4, 2024

Язык: Английский

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Research on competition score prediction based on GA-BP neural network model and RBP inverse neural network model

2022 International Conference on Electronics and Devices, Computational Science (ICEDCS), Год журнала: 2024, Номер unknown, С. 562 - 566

Опубликована: Сен. 23, 2024

Язык: Английский

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