10 papers
L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
Fan Zhang, Shijun Chen, Hua Wang
Mainstream methods for multivariate time-series forecasting largely follow the Direct-Mapping paradigm. They learn a unified mapping from history to the future in the observation s…
CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification
Fan Zhang, Yating Cui, Hua Wang
Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are often constrained by suboptimal…
MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
Hanjun Tao, Hua Wang, Fan Zhang
Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience-dependent, thus necessitating…
PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
Hua Wang, Xianhao Jiao, Fan Zhang
Deep forecasting models often suffer from attenuated periodic perception and entangled trend-noise representations as network depth increases. Moreover, the widely adopted channel-…
TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
Fan Zhang, Shiming Fan, Hua Wang
Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion strategy, where dense interaction…
Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting
Fan Zhang, Shiming Fan, Hua Wang
Time series forecasting is crucial for the World Wide Web and represents a core technical challenge in ensuring the stable and efficient operation of modern web services, such as i…