21 citations · 29 across the 5 of their papers we have counts for
5 papers
CoRA: Covariate-Aware Adaptation of Time Series Foundation Models
Guo Qin, Zhi Chen, Yong Liu +5
Time Series Foundation Models (TSFMs) have shown significant impact through their model capacity, scalability, and zero-shot generalization. However, due to the heterogeneity of in…
Sundial: A Family of Highly Capable Time Series Foundation Models
Yong Liu, Guo Qin, Zhiyuan Shi +5
We introduce Sundial, a family of native, flexible, and scalable time series foundation models. To predict the next-patch's distribution, we propose a TimeFlow Loss based on flow-m…
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
Yong Liu, Guo Qin, Xiangdong Huang +2
We present Timer-XL, a causal Transformer for unified time series forecasting. To uniformly predict multidimensional time series, we generalize next token prediction, predominantly…
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
Yong Liu, Guo Qin, Xiangdong Huang +2
Foundation models of time series have not been fully developed due to the limited availability of time series corpora and the underexploration of scalable pre-training. Based on th…
Efficient ConvBN Blocks for Transfer Learning and Beyond
Kaichao You, Guo Qin, Anchang Bao +4
Convolution-BatchNorm (ConvBN) blocks are integral components in various computer vision tasks and other domains. A ConvBN block can operate in three modes: Train, Eval, and Deploy…