5 papers
Towards Robust Time Series Learning via Capacity-Centric Modulation
Siru Zhong, Senzhang Wang, James T. Kwok +1
Sample-level reliability heterogeneity is common in deep time series learning. Standard training pipelines apply a uniform regularization setting to all samples, which can under-re…
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
Ziyu Zhou, Tian Zhou, Yuchen Fang +4
Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remains challenging due to chaotic atmospher…
SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecasting
Ziyu Zhou, Yuchen Fang, Weilin Ruan +3
Telemetry streams from large-scale Internet-connected systems (e.g., IoT deployments and online platforms) naturally form an irregular multivariate time series (IMTS) whose accurat…
Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting
Ziyu Zhou, Yiming Huang, Yanyun Wang +3
Irregular multivariate time series (IMTS), characterized by uneven sampling and inter-variate asynchrony, fuel many forecasting applications yet remain challenging to model efficie…
Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting
Ziyu Zhou, Jiaxi Hu, Qingsong Wen +2
In deep time series forecasting, the Fourier Transform (FT) is extensively employed for frequency representation learning. However, it often struggles in capturing multi-scale, tim…