8 papers
REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting
Xu Zhang, Chang Xu, Hui Sun +5
Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples. Ensemble learning addresses this by combining complementary m…
SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
Xu Zhang, Qitong Wang, Peng Wang +1
Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent…
Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity
Xu Zhang, Zhengang Huang, Yunzhi Wu +6
Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges. However, in re…
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
Xu Zhang, Peng Wang, Yichen Li +1
Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to trai…
A Lightweight Sparse Interaction Network for Time Series Forecasting
Xu Zhang, Qitong Wang, Peng Wang +1
Recent work shows that linear models can outperform several transformer models in long-term time-series forecasting (TSF). However, instead of explicitly performing temporal intera…
Global Feature Enhancing and Fusion Framework for Strain Gauge Time Series Classification
Xu Zhang, Peng Wang, Chen Wang +3
Strain Gauge Status (SGS) time series recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely dete…