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.
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...
arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.
arXiv:2609.24156v1 Announce Type: cross Abstract: Most existing time series forecasting methods rely solely on numerical observations, overlooking rich contextual information from auxiliary texts. Re...
arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.
SCENARIODIFF is a hierarchical contextual reasoning framework designed for multimodal time series forecasting, especially in event-driven domains. It processes textual context through three agents—Historical Context, Scenario, and Anchor Guidance—to generate structured signals that condition a Multimodal Diffusion Transformer. The framework also employs Anchor Blended Sampling to locally refine forecast trajectories without retraining, and demonstrates superior performance on the Time‑MMD benchmark.
The paper introduces Expert Modulation, a novel approach for multi‑modal time series prediction that conditions both expert routing and computation on textual signals, thereby providing direct cross‑modal control over expert behavior. Unlike previous methods that rely on token‑level fusion, this mechanism avoids mixing temporal patches with language tokens in a shared embedding space, which can be problematic when high‑quality time‑text pairs are scarce or when time series characteristics vary widely. Experiments and theoretical analysis demonstrate that Expert Modulation yields strong improvements over existing multi‑modal forecasting techniques.
arXiv:2603. 05997v2 Announce Type: replace-cross Abstract: Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables.
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:2606. 06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks.
arXiv:2602. 01588v3 Announce Type: replace-cross Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals.
Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not yet visible in hist...
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.