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cs.LG2024
Towards Neural Scaling Laws for Time Series Foundation Models
Qingren Yao, Chao-Han Huck Yang, Renhe Jiang +3
Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-di…
cs.LG2024
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Zheng Dong, Renhe Jiang, Haotian Gao +4
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and lever…
cs.LG2023
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Feiyi Chen, Yingying zhang, Zhen Qin +5
Anomaly detection significantly enhances the robustness of cloud systems. While neural network-based methods have recently demonstrated strong advantages, they encounter practical…