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…
Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers
Yuanxu Sun, Yuezhou Ma, Haixu Wu +4
Boundary representation (B-rep) is the industry standard for computer-aided design (CAD). While deep learning shows promise in processing B-rep models, existing methods suffer from…
Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
Hang Zhou, Haixu Wu, Haonan Shangguan +4
Deep learning has emerged as a transformative tool for the neural surrogate modeling of partial differential equations (PDEs), known as neural PDE solvers. However, scaling these s…
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…
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…