7 papers · 1 filter
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Yaxuan Kong, Yuxuan Liang +13
Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record d…
GenTS: A Comprehensive Benchmark Library for Generative Time Series Models
Chenxi Wang, Xiaorong Wang, Peiyang Li +1
Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering limited coverage for generat…
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Yiyuan Yang, Ming Jin, Haomin Wen +9
Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across dive…
Task-oriented Time Series Imputation Evaluation via Generalized Representers
Zhixian Wang, Linxiao Yang, Liang Sun +2
Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classifica…
Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
Dalin Qin, Yehui Li, Weiqi Chen +5
Complex distribution shifts are the main obstacle to achieving accurate long-term time series forecasting. Several efforts have been conducted to capture the distribution character…
DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
Zhixian Wang, Qingsong Wen, Chaoli Zhang +2
Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources…