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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…
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…
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…