3 accepted papers
Large time series models (LTMs) have recently demonstrated powerful capabilities for universal forecasting. However, these models still struggle to address the variety and nonstationarity of time series, resulting in an unsatisfying balance between forecasting performance and generalizability. Inste…
Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. Prior methods are mainly based on pre-training techniques well-acknowledged in vision or language, such as masked modeling and contrastive learning. Howev…
Deep models have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a…