5 citations · 9 across the 5 of their papers we have counts for
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…
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Yifan Hu +4
Non-stationarity poses significant challenges for multivariate time series forecasting due to the inherent short-term fluctuations and long-term trends that can lead to spurious re…
Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting
Jiarui Yang, Tao Dai, Naiqi Li +6
In recent years, generative pre-trained paradigms such as Large Language Models (LLMs) and Large Vision Models (LVMs) have achieved revolutionary advancements and widespread real-w…
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Peiyuan Liu, Hang Guo, Tao Dai +5
Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a…
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Naiqi Li +5
Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourie…