8 papers
FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
Qianyang Li, Xingjun Zhang, Shaoxun Wang +2
Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual s…
HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
Jia Wei, Zhonghao Zhang, Ping Chen +5
Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning of large language models, yet most variants target dense architectures. Mixture-of-Experts (MoE) models scale p…
ASGMamba: Adaptive Spectral Gating Mamba for Multivariate Time Series Forecasting
Qianyang Li, Xingjun Zhang, Shaoxun Wang +2
Long-term multivariate time series forecasting (LTSF) plays a crucial role in various high-performance computing applications, including real-time energy grid management and large-…
SDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
Shaoxun Wang, Xingjun Zhang, Qianyang Li +2
Accurate multivariate time series forecasting hinges on inter-series correlations, which often evolve in complex ways across different temporal scales. Existing methods are limited…
DPWMixer: Dual-Path Wavelet Mixer for Long-Term Time Series Forecasting
Li Qianyang, Zhang Xingjun, Wang Shaoxun +1
Long-term time series forecasting (LTSF) is a critical task in computational intelligence. While Transformer-based models effectively capture long-range dependencies, they often su…
D-CTNet: A Dual-Branch Channel-Temporal Forecasting Network with Frequency-Domain Correction
Shaoxun Wang, Xingjun Zhang, Kun Xia +3
Accurate Multivariate Time Series (MTS) forecasting is crucial for collaborative design of complex systems, Digital Twin building, and maintenance ahead of time. However, the colla…