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[HTML][HTML] Pro Trader RL: Reinforcement learning framework for generating trading knowledge by mimicking the decision-making patterns of professional traders
YH Gu - Expert Systems with Applications, 2024 - Elsevier
This study proposes a novel reinforcement learning (RL) framework, professional trader RL
(Pro Trader RL), which mimics the decision-making patterns and trading philosophy of …
(Pro Trader RL), which mimics the decision-making patterns and trading philosophy of …
Unveiling the potential: Exploring the predictability of complex exchange rate trends
Forecasting exchange rates is challenging due to its diverse features and complex patterns.
Inspired the theory of receptive fields, we proposed two models: the Transformer with …
Inspired the theory of receptive fields, we proposed two models: the Transformer with …
A deep fusion model for stock market prediction with news headlines and time series data
Time series forecasting models are essential decision support tools in real-world domains.
Stock market is a remarkably complex domain, due to its quickly evolving temporal nature …
Stock market is a remarkably complex domain, due to its quickly evolving temporal nature …
Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions
Time series analysis has been widely employed in various domains, including finance,
healthcare, meteorology, and economics. This approach is crucial in extracting patterns …
healthcare, meteorology, and economics. This approach is crucial in extracting patterns …
Stock complex networks based on the GA-LightGBM model: The prediction of firm performance
One of the fundamental issues in predicting firm performance from the perspective of
complex systems is how to accurately construct stock networks. Most stock network-based …
complex systems is how to accurately construct stock networks. Most stock network-based …
Stock price nowcasting and forecasting with deep learning
Recent studies have improved stock price forecasting with the emerging deep learning
models. Despite advancements in deep learning, stock price prediction faces significant …
models. Despite advancements in deep learning, stock price prediction faces significant …
A Hybrid Relational Approach Towards Stock Price Prediction and Profitability
An accurate estimation of future stock prices can help investors maximize their profits. The
current advancements in the area of artificial intelligence (AI) have proven prevalent in the …
current advancements in the area of artificial intelligence (AI) have proven prevalent in the …
VMD-ConvTSMixer: Spatiotemporal channel mixing model for non-stationary time series forecasting
Time series forecasting is a cornerstone in domains such as weather prediction, traffic flow
analysis, and industrial process control, playing a vital role in resource optimization and …
analysis, and industrial process control, playing a vital role in resource optimization and …
PMformer: A novel informer-based model for accurate long-term time series prediction
Y Xue, S Guan, W Jia - Information Sciences, 2025 - Elsevier
When applied to long-term time series forecasting, Informer struggles to capture temporal
dependencies effectively, leading to suboptimal forecasting accuracy. To address this issue …
dependencies effectively, leading to suboptimal forecasting accuracy. To address this issue …
Using dynamic semantic structure of news flow to enhance financial forecasting: a twelve-year study on twitter news channels
This research holds significance for advancing financial forecasting methodologies by
shifting the focus from traditional sentiment analysis of individual tweets to exploring intricate …
shifting the focus from traditional sentiment analysis of individual tweets to exploring intricate …