500 citations · 2k across the 65 of their papers we have counts for
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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,…
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…
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting
Jincheng Zhong, Xingzhuo Guo, Jiaxiang Dong +1
Diffusion models have significantly advanced the field of generative modeling. However, training a diffusion model is computationally expensive, creating a pressing need to adapt o…
CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding
Kaiyuan Chen, Xingzhuo Guo, Yu Zhang +2
Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of the visual world. Ex…
Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
Hang Zhou, Yuezhou Ma, Haixu Wu +2
Deep models have recently emerged as promising tools to solve partial differential equations (PDEs), known as neural PDE solvers. While neural solvers trained from either simulatio…
iVideoGPT: Interactive VideoGPTs are Scalable World Models
Jialong Wu, Shaofeng Yin, Ningya Feng +4
World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interac…
RoPINN: Region Optimized Physics-Informed Neural Networks
Haixu Wu, Huakun Luo, Yuezhou Ma +2
Physics-informed neural networks (PINNs) have been widely applied to solve partial differential equations (PDEs) by enforcing outputs and gradients of deep models to satisfy target…
CompilerDream: Learning a Compiler World Model for General Code Optimization
Chaoyi Deng, Jialong Wu, Ningya Feng +2
Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the…
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
Kaichao You, Runsheng Bai, Meng Cao +3
PyTorch \texttt{2.x} introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, adapting to the PyTorch compiler to full potent…
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…
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
Jiaxiang Dong, Haixu Wu, Yuxuan Wang +4
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…
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
Qilong Ma, Haixu Wu, Lanxiang Xing +2
Accurately predicting the future fluid is vital to extensive areas such as meteorology, oceanology, and aerodynamics. However, since the fluid is usually observed from the Eulerian…
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…
Transolver: A Fast Transformer Solver for PDEs on General Geometries
Haixu Wu, Huakun Luo, Haowen Wang +2
Transformers have empowered many milestones across various fields and have recently been applied to solve partial differential equations (PDEs). However, since PDEs are typically d…