arXiv Machine Learning

WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting

WorldTS is a new forecasting framework that models latent dynamics conditioned on multimodal covariates to improve time‑series prediction. It uses a two‑stage training process: first learning latent state dynamics from historical data and covariates, then training a decoder to map predicted latent states back to future observations. Experiments on 21 real‑world datasets demonstrate the effectiveness of this approach.

arXiv Machine Learning

Aug 19

TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts

The paper introduces TiMi, a framework that enhances time series transformers with a Multimodal Mixture-of-Experts (MMoE) module to incorporate multimodal data, especially textual information, into forecasting. TiMi leverages large language models to generate future inferences that guide predictions, eliminating the need for explicit representation alignment. Experiments show TiMi achieves state‑of‑the‑art performance on sixteen real‑world multimodal forecasting benchmarks, outperforming advanced baselines while maintaining adaptability and interpretability.

By Jiafeng Lin, Yuxuan Wang, Huakun Luo, Jianmin Wang, Zhongyi Pei

llms multimodal benchmarks safety

arXiv AI

Sep 15

Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context

arXiv:2609.15087v1 Announce Type: cross Abstract: Most time series forecasting benchmarks remain numerical-centric and provide limited support for evaluating contextual information that shapes real-w...

By Peng Chen, Zhihao Zhuang, Hongzhou Chen, Junhao Huang, Aiping Yang, Mengsen Wu, Yiding Liu, Xilin Dai, Zewei Dong

llms multimodal benchmarks

arXiv AI

Jun 16

Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting

arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.

By Weizhi Nie, Weichao Liu, Honglin Guo, Yuting Su

rag

arXiv Machine Learning

Aug 21

CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting

arXiv:2608. 20025v1 Announce Type: new Abstract: Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation.

By Alexander Marusov, Dmitry Anikin, Petr Sokerin, Vitaliy Pozdnyakov, Ilya Kuleshov, Alexey Zaytsev

rag benchmarks

arXiv Machine Learning

Jul 9

Rethinking Multimodal Time-Series Forecasting Evaluation

arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.

By Haoxin Liu, Yichen Zhou, Rajat Sen, B. Aditya Prakash, Abhimanyu Das

multimodal benchmarks

arXiv Machine Learning

Jun 8

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

arXiv:2606. 07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences.

By Tao Chen, Yexu Zhou, Zhi Gong, Hengwei He, Hongda Li, Zhewei Chen, Dongjing Wang, Xin Zhang, Decheng Liu, Chunlei Peng, Zheng Chen, Wenyue Ding

benchmarks

arXiv AI

Aug 18

Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment

arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.

By Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu, Qinghua Hu, Chee Keong Kwoh, Min Wu

llms benchmarks safety

arXiv AI

Jun 30

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

arXiv:2604. 16325v3 Announce Type: replace-cross Abstract: Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal dependencies and cross-variable interactions pose enduring challenges.

By Xingsheng Chen, Xianpei Mu, Deyu Yi, Yilin Yuan, Xingwei He, Bo Gao, Regina Zhang, Pietro Lio, Siu-Ming Yiu

llms benchmarks

arXiv AI

Jul 23

Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting

arXiv:2607. 19404v1 Announce Type: cross Abstract: Multivariate time series encode structural patterns that unfold across multiple temporal scales, yet most forecasting backbones treat learned representations as transient byproducts of prediction, leaving the organizational geometry of these patterns underexploited.

By Xingsheng Chen, Deyu Yi, Siu-Ming Yiu

diffusion benchmarks safety

arXiv AI

Jun 10

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.

By Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei

llms

arXiv AI

Jun 9

VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion

arXiv:2510. 03244v2 Announce Type: replace-cross Abstract: Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies.

By Yanlong Wang, Hang Yu, Jian Xu, Fei Ma, Hongkang Zhang, Tongtong Feng, Zijian Zhang, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang

multimodal benchmarks

arXiv Machine Learning

Aug 13

FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting

arXiv:2608. 11254v1 Announce Type: new Abstract: Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids.

By Yann Fabel, Bijan Nouri, Milon Miah, Niklas Blum, Luis F. Zarzalejo, Julia Kowalski, Robert Pitz-Paal

diffusion benchmarks

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