5 papers
FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction
Yifan Hu, Peiyuan Liu, Yuante Li +5
Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents signific…
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
Yifan Hu, Yuante Li, Peiyuan Liu +6
Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investmen…
From Observations to States: Latent Time Series Forecasting
Jie Yang, Yifan Hu, Yuante Li +3
Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate pr…
MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU
Peng Zhu, Yuante Li, Yifan Hu +4
As financial markets grow increasingly complex in the big data era, accurate stock prediction has become more critical. Traditional time series models, such as GRUs, have been wide…
LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU
Peng Zhu, Yuante Li, Yifan Hu +3
Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep…