# UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting

arXiv: https://arxiv.org/abs/2512.07184 · HTML: https://mlsanity.com/paper/2512.07184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 53 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://mlsanity.com/paper/2404.18886/) (2024)
- [SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2308.11200/) (2023)
- [Aurora: Towards Universal Generative Multimodal Time Series Forecasting](https://mlsanity.com/paper/2509.22295/) (2025)
- [Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data](https://mlsanity.com/paper/2411.06735/) (2024)
- [T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models](https://mlsanity.com/paper/2505.02417/) (2025)
- [Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation](https://mlsanity.com/paper/2502.01477/) (2025)
- [Multimodal Conditioned Diffusive Time Series Forecasting](https://mlsanity.com/paper/2504.19669/) (2025)
- [MoTime: A Dataset Suite for Multimodal Time Series Forecasting](https://mlsanity.com/paper/2505.15072/) (2025)
- [Fusing Large Language Models with Temporal Transformers for Time Series Forecasting](https://mlsanity.com/paper/2507.10098/) (2025)
- [Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts](https://mlsanity.com/paper/2505.01135/) (2025)
- [MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic](https://mlsanity.com/paper/2507.00304/) (2025)
- [Context Matters: Leveraging Contextual Features for Time Series Forecasting](https://mlsanity.com/paper/2410.12672/) (2024)
- [UniCast: A Unified Framework for Instance-Conditioned Multimodal Time-Series Forecasting](https://mlsanity.com/paper/2508.11954/) (2025)
- [Domain Fusion Controllable Generalization for Cross-Domain Time Series Forecasting from Multi-Domain Integrated Distribution](https://mlsanity.com/paper/2412.03068/) (2024)

## Built on by (most cited first, 1 of 1)

- [SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version](https://mlsanity.com/paper/2608.17164/) (2026)

## Abstract (arXiv)

As multimodal data proliferates across diverse real-world applications, leveraging heterogeneous information such as texts and timestamps for accurate time series forecasting (TSF) has become a critical challenge. While diffusion models demonstrate exceptional performance in generation tasks, their application to TSF remains largely confined to modeling single-modality numerical sequences, overlooking the abundant cross-modal signals inherent in complex heterogeneous data. To address this gap, we propose UniDiff, a unified diffusion framework for multimodal time series forecasting. To process the numerical sequence, our framework first tokenizes the time series into patches, preserving local temporal dynamics by mapping each patch to an embedding space via a lightweight MLP. At its core lies a unified and parallel fusion module, where a single cross-attention mechanism adaptively weighs and integrates structural information from timestamps and semantic context from texts in one step, enabling a flexible and efficient interplay between modalities. Furthermore, we introduce a novel classifier-free guidance mechanism designed for multi-source conditioning, allowing for decoupled control over the guidance strength of textual and temporal information during inference, which significantly enhances model robustness. Extensive experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.