383 citations · 469 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Deep Time Series Models: A Comprehensive Survey and Benchmark
Yuxuan Wang, Haixu Wu, Jiaxiang Dong +4
Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other data modalities, time series presen…
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
Yuxuan Wang, Haixu Wu, Jiaxiang Dong +6
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 ta…
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…
Timer: Generative Pre-trained Transformers Are Large Time Series Models
Yong Liu, Haoran Zhang, Chenyu Li +3
Deep learning has contributed remarkably to the advancement of time series analysis. Still, deep models can encounter performance bottlenecks in real-world data-scarce scenarios, w…