51 citations · 52 across the 9 of their papers we have counts for
7 papers · 1 filter
Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
Jiafeng Lin, Mengren Zheng, Simeng Ye +5
Time series forecasting has witnessed an increasing demand across diverse industrial applications, where accurate predictions are pivotal for informed decision-making. Beyond numer…
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
Haoran Zhang, Haixuan Liu, Yong Liu +4
While generative modeling on time series facilitates more capable and flexible probabilistic forecasting, existing generative time series models do not address the multi-dimensiona…
Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
Yuxuan Wang, Haixu Wu, Yuezhou Ma +8
Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often…
Metadata Matters for Time Series: Informative Forecasting with Transformers
Jiaxiang Dong, Haixu Wu, Yuxuan Wang +3
Time series forecasting is prevalent in extensive real-world applications, such as financial analysis and energy planning. Previous studies primarily focus on time series modality,…
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…