5 papers
Repetitive Contrastive Learning Enhances Mamba's Selectivity in Time Series Prediction
Wenbo Yan, Hanzhong Cao, Ying Tan
Long sequence prediction is a key challenge in time series forecasting. While Mamba-based models have shown strong performance due to their sequence selection capabilities, they st…
Numerion: A Multi-Hypercomplex Model for Time Series Forecasting
Hanzhong Cao, Wenbo Yan, Ying Tan
Many methods aim to enhance time series forecasting by decomposing the series through intricate model structures and prior knowledge, yet they are inevitably limited by computation…
Hierarchical Information-Guided Spatio-Temporal Mamba for Stock Time Series Forecasting
Wenbo Yan, Shurui Wang, Ying Tan
Mamba has demonstrated excellent performance in various time series forecasting tasks due to its superior selection mechanism. Nevertheless, conventional Mamba-based models encount…
Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting
Wenbo Yan, Ying Tan
Spatial-temporal graph neural networks (STGNNs) have achieved significant success in various time series forecasting tasks. However, due to the lack of explicit and fixed spatial r…
TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting
Wenbo Yan, Ying Tan
Recently, the incorporation of both temporal features and the correlation across time series has become an effective approach in time series prediction. Spatio-Temporal Graph Neura…