6 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…
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-…
Dual-pronged deep learning preprocessing on heterogeneous platforms with CPU, Accelerator and CSD
Jia Wei, Xingjun Zhang, Witold Pedrycz +2
For image-related deep learning tasks, the first step often involves reading data from external storage and performing preprocessing on the CPU. As accelerator speed increases and…
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…
AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting
Qianyang Li, Xingjun Zhang, Peng Tao +3
Forecasting long-term time series in IoT environments remains a significant challenge due to the non-stationary and multi-scale characteristics of sensor signals. Furthermore, erro…
DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting
Qianyang Li, Xingjun Zhang, Shaoxun Wang +1
Long-term time series forecasting (LTSF) is hampered by the challenge of modeling complex dependencies that span multiple temporal scales and frequency resolutions. Existing method…