Aurora: Towards Universal Generative Multimodal Time Series Forecasting
Aurora is a multimodal time series foundation model supporting zero-shot cross-domain forecasting, using flow matching and prototype-guided mechanisms.
Key Findings
Methodology
Aurora pretrains on a cross-domain multimodal corpus combining time series, text, and images. It employs tokenization, encoding, and distillation to extract domain knowledge. The model integrates modality-guided multi-head self-attention to fuse multimodal features into temporal representations. During decoding, a condition decoder generates future conditions, while a prototype bank and retriever provide initial prototypes for flow matching. The core algorithms include Transformer encoders, flow matching, and prototype retrieval, enabling robust cross-domain generalization and generative probabilistic forecasting.
Key Results
- On benchmarks like TimeMMD, TSFM-Bench, ProbTS, TFB, and EPF, Aurora outperforms SOTA models with over 15% reduction in mean squared error (MSE). In zero-shot cross-domain prediction, MSE improves by 20% across datasets. In few-shot (10%) scenarios, Aurora maintains superior performance, reducing MSE by 12.8% compared to full supervised models.
- In modality-absent settings, Aurora achieves state-of-the-art results on deterministic and probabilistic tasks, with average CRPS reductions of 18% over Time-MoE and ROSE. Its robustness in short-term forecasting on EPF and TFB datasets surpasses traditional models, demonstrating strong adaptability.
- Ablation studies confirm that modality-guided attention and prototype flow matching are key to performance gains, especially in cross-domain and low-data scenarios.
Significance
This work advances time series forecasting by enabling effective cross-domain and multimodal generalization, addressing longstanding challenges of knowledge transfer and robustness. Aurora's generative capabilities and zero-shot inference significantly broaden the application scope in finance, meteorology, transportation, and beyond. It represents a major step toward versatile, intelligent decision support systems, fostering theoretical and practical progress in multimodal deep learning for temporal data.
Technical Contribution
Aurora innovatively combines cross-domain pretraining on multimodal corpora, modality-guided attention, and prototype-guided flow matching. The model introduces a novel fusion mechanism that enhances multimodal information integration and a stable, interpretable generation process via prototypes. These contributions enable robust, zero-shot cross-domain probabilistic forecasting, setting new benchmarks in the field.
Novelty
This is the first framework to support zero-shot, cross-domain multimodal time series forecasting by integrating multimodal knowledge during pretraining and employing prototype-guided flow matching. Unlike prior models limited to single modality or supervised learning, Aurora's architecture allows out-of-the-box adaptation to new domains with minimal data, marking a significant innovation in the field.
Limitations
- Aurora's performance may decline in highly non-stationary or abrupt change scenarios due to limitations in pretraining data coverage. Its computational complexity and resource demands restrict deployment on edge devices. Multimodal data reliance can introduce noise, affecting accuracy. Future work should focus on efficiency and robustness improvements.
Future Work
Future directions include integrating self-supervised learning to enhance knowledge extraction, developing dynamic multimodal fusion techniques, and reducing computational costs. Extending the model to handle real-time streaming data and exploring adaptive prototype mechanisms are also promising avenues to broaden its practical impact.
AI Executive Summary
Aurora represents a significant breakthrough in time series forecasting, addressing the critical challenge of cross-domain and multimodal generalization. Traditional models, while effective within specific domains, struggle to adapt when faced with new environments or limited data. Aurora overcomes this by pretraining on a large, cross-domain multimodal corpus that includes time series, text, and images, enabling it to learn rich, domain-specific knowledge.
The model employs a sophisticated architecture combining Transformer encoders, modality-guided attention, and a novel flow matching mechanism guided by prototypes. During training, Aurora distills key information from multiple modalities, effectively fusing them into a unified temporal representation. The decoder then leverages a condition generator and a prototype bank, retrieving relevant prototypes to guide the flow-based prediction process. This approach allows Aurora to generate accurate probabilistic forecasts even in zero-shot scenarios, where no domain-specific training data is available.
Experimental results across five benchmark datasets demonstrate Aurora’s superior performance. It reduces mean squared error by over 15% compared to existing state-of-the-art models, maintains robustness in few-shot settings, and excels in modality-absent tasks. The ablation studies highlight the importance of multimodal attention and prototype guidance, confirming their roles in performance enhancement.
Aurora’s broader impact lies in its ability to facilitate decision-making in complex, data-scarce environments, such as climate prediction, financial analysis, and traffic management. Its design paves the way for future research into more efficient, adaptive, and scalable multimodal forecasting systems. Despite current limitations in computational efficiency and handling abrupt changes, ongoing improvements promise to extend Aurora’s applicability, making it a cornerstone for next-generation intelligent systems in temporal data analysis.
Deep Analysis
Background
时间序列预测作为关键的决策支持工具，经历了从传统统计模型到深度学习模型的快速发展。早期方法如ARIMA、HMM在线性和简单非线性场景中表现良好，但难以捕获复杂动态。近年来，Transformer、Autoformer、TimesNet等深度模型通过学习数据的内在规律，显著提升了预测性能。与此同时，跨域和多模态信息的引入成为研究热点。VisionTS、ROSE等模型在单模态基础上实现跨域适应，但缺乏多模态知识融合。端到端多模态模型如GPT4MTS、TATS虽能融合多模态信息，但多为监督学习，缺少零样本泛化能力。Aurora的出现，结合预训练、多模态融合和生成机制，推动了时间序列预测的多模态发展。
Core Problem
现有模型在跨域、少样本和多模态场景中表现不足，主要原因在于缺乏对领域知识的显式利用和多模态信息的高效融合。单模态模型难以实现知识迁移，端到端模型在新领域或数据不足时性能下降。如何设计支持零样本、跨域、多模态的基础模型，成为核心难题。这关系到模型的泛化能力、鲁棒性和实用性，亟需创新技术方案。
Innovation
Aurora的创新点包括：1）预训练于跨域多模态语料库，融合文本和图像模态，显著提升跨域适应性；2）引入模态引导多头自注意力机制，有效融合多模态信息，增强时间表示；3）采用原型引导的Flow Matching机制，提供稳定、直观的生成起点，突破高斯噪声初始化限制。这些创新共同推动了时间序列基础模型的多模态和生成能力。
Methodology
- �� 利用跨域多模态语料库，结合时间序列、文本和图像模态，采用Tokenization、编码和蒸馏提取关键知识。
- �� 通过VisionDistiller和TextDistiller实现模态信息的压缩与融合，利用Cross-Attention捕获模态间关系。
- �� 引入Modality-Guided Multi-head Self-Attention，将模态信息注入时间建模，增强跨域泛化。
- �� 条件解码器生成未来条件，结合Prototype Bank和Retriever，利用原型引导流匹配实现生成。
- �� 流匹配学习速度场，从随机初始化到目标分布，结合原型提供稳定起点，提升生成效率。
Experiments
在TimeMMD、TSFM-Bench、ProbTS、EPF和TFB数据集上，进行零样本和少样本预测性能评估。对比SOTA模型如Sundial、VisionTS，采用MSE、CRPS等指标验证优越性。设计消融实验验证模态引导机制和原型流匹配的贡献。调优模型参数，确保公平比较，强调跨域和多模态场景的适应性。
Results
Aurora在五个基准数据集上均优于现有模型，平均MSE降低15%以上。零样本预测中，MSE提升达20%，表现尤为突出。少样本条件下，性能优于全监督模型，MSE降低12.8%。消融实验显示模态引导和原型机制是性能提升的关键，验证其有效性。
Applications
该模型适用于金融、气象、交通等行业，尤其在数据不足或跨域环境中表现出色。其生成能力支持未来趋势预测和异常检测，为智能决策提供工具。部署需高性能计算资源，但可通过优化模型结构降低成本。
Limitations & Outlook
在极端非平稳或突变场景中表现有限，预训练语料覆盖不足限制其效果。高计算成本限制在边缘设备应用。多模态依赖可能引入噪声，影响预测准确性。未来需优化模型结构和训练策略。
Plain Language Accessible to non-experts
想象你在一个工厂工作，每天都要预测未来的生产需求。以前只用过去的订单数据，但这时常不准，因为订单会受到天气、市场等因素影响。Aurora就像一个聪明的助手，不仅看过去的订单，还能结合天气预报、市场新闻等信息，提前帮你预测未来的需求。它学习了很多不同工厂的经验，能在没有新数据的情况下，快速给出合理的预测。它还能理解不同信息之间的关系，比如天气变冷可能会增加能源需求。这样一来，工厂的决策就更科学、更稳妥了。它的核心，就是用多种信息融合的方法，让预测变得更智能、更准确。
ELI14 Explained like you're 14
想象你在学校的图书馆里，想知道明天会不会下雨。以前，你只看昨天的天气，但这不够准，因为天气还受到风、云、气温等很多因素影响。现在，有个超级聪明的机器人助手，它不仅看昨天的天气，还会看天气预报、天空的照片和天气说明书。它提前学会了怎么结合这些信息，帮你预测明天会不会下雨。这个机器人特别聪明，因为它学会了用不同的线索一起推断，而不是只看一个线索。它还能在没有新信息的情况下，凭借以前学到的知识，做出合理的猜测。这样，你就可以更放心地准备雨伞啦！
Abstract
Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinct future trends due to the domain-specific characteristics. Recent works focus on building unimodal time series foundation models and end-to-end multimodal supervised models. Since domain-specific knowledge is often contained in modalities like texts, the former lacks the explicit utilization of them, thus hindering the performance. The latter is tailored for end-to-end scenarios and does not support zero-shot inference for cross-domain scenarios. In this work, we introduce Aurora, a Multimodal Time Series Foundation Model, which supports multimodal inputs and zero-shot inference. Pretrained on Cross-domain Multimodal Time Series Corpus, Aurora can adaptively extract and focus on key domain knowledge contained in corresponding text or image modalities, thus possessing strong cross-domain generalization capability. Through tokenization, encoding, and distillation, Aurora can extract multimodal domain knowledge as guidance and then utilizes a Modality-Guided Multi-head Self-Attention to inject them into the modeling of temporal representations. In the decoding phase, the multimodal representations are used to generate the conditions and prototypes of future tokens, contributing to a novel Prototype-Guided Flow Matching for generative probabilistic forecasting. Comprehensive experiments on 5 well-recognized benchmarks, including TimeMMD, TSFM-Bench, ProbTS, TFB, and EPF, demonstrate the consistent state-of-the-art performance of Aurora on both unimodal and multimodal scenarios.