https://github.com/forestsking/awesome-multimodal-time-series
A curated list of paper, code, data, and other resources focus on multimodal time series analysis.
https://github.com/forestsking/awesome-multimodal-time-series
List: awesome-multimodal-time-series
awesome awesome-list awesome-resources multimodal-deep-learning multimodal-time-series multimodality time-series time-series-analysis time-series-forecasting
Last synced: 21 days ago
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A curated list of paper, code, data, and other resources focus on multimodal time series analysis.
- Host: GitHub
- URL: https://github.com/forestsking/awesome-multimodal-time-series
- Owner: ForestsKing
- Created: 2024-11-12T13:07:20.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-30T11:46:21.000Z (28 days ago)
- Last Synced: 2025-05-06T23:02:00.776Z (21 days ago)
- Topics: awesome, awesome-list, awesome-resources, multimodal-deep-learning, multimodal-time-series, multimodality, time-series, time-series-analysis, time-series-forecasting
- Homepage:
- Size: 9.77 KB
- Stars: 71
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Awesome Multimodal Time Series Analysis
[](https://awesome.re)
A curated list of paper, code, data, and other resources focus on multimodal time series analysis.
🚀 This repo will be continuously updated.
⭐️ Please Star it if you find it helpful!
🤝 Feel free to submit a PR or open an issue with suggestions or improvements.
---
## Table of Contents
- [Time Series Forecasting](#Time-Series-Forecasting)
- [Time Series Reasoning](#Time-Series-Reasoning)
- [Time Series Captioning](#Time-Series-Captioning)
- [Time Series Alignment](#Time-Series-Alignment)
---
## Time Series Forecasting
| Title | Venue | Month | Code |
|:---------------------------------------------------------------------------------------------------------------------------------------------------|:-------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|
| [Multimodal Conditioned Diffusive Time Series Forecasting](https://arxiv.org/abs/2504.19669) | arXiv | 2025.04 | [Github](https://github.com/synlp/MCD-TSF) |
| [Exploring the Effectiveness and Interpretability of Texts in LLM-based Time Series Models](https://arxiv.org/abs/2504.08808) | arXiv | 2025.04 | [Github](https://github.com/zachysun/TS-Lang-Exp) |
| [Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop](https://arxiv.org/abs/2503.01013) | arXiv | 2025.03 | None |
| [Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time-Series Forecasting: A Benchmark and Insights](https://arxiv.org/abs/2503.01895) | arXiv | 2025.02 | [Github](https://github.com/AdityaLab/OpenTimeR) |
| [TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents](https://arxiv.org/abs/2502.11418) | AAAI | 2025.02 | None |
| [Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative](https://arxiv.org/abs/2502.08942) | arXiv | 2025.02 | None |
| [ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data](https://arxiv.org/abs/2412.11376) | AAAI | 2024.12 | [Github](https://github.com/ForestsKing/ChatTime) |
| [Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data](https://arxiv.org/abs/2411.06735) | arXiv | 2024.11 | [Github](https://github.com/Rose-STL-Lab/Multimodal_Forecasting) |
| [Context is Key: A Benchmark for Forecasting with Essential Textual Information](https://arxiv.org/abs/2410.18959) | arXiv | 2024.10 | [Github](https://github.com/ServiceNow/context-is-key-forecasting) |
| [From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection](https://arxiv.org/abs/2409.17515) | NeurIPS | 2024.09 | [Github](https://github.com/ameliawong1996/From_News_to_Forecast) |
| [Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis](https://arxiv.org/abs/2406.08627) | NeurIPS | 2024.06 | [Github](https://github.com/AdityaLab/Time-MMD) |
| [Beyond Trend and Periodicity: Guiding Time Series Forecasting with Textual Cues](https://arxiv.org/abs/2405.13522) | arXiv | 2024.05 | [Github](https://github.com/vewoxic/tgtsf) |
| [Language Models Still Struggle to Zero-shot Reason about Time Series](https://arxiv.org/abs/2404.11757) | arXiv | 2024.04 | [Github](https://github.com/behavioral-data/TSandLanguage) |
| [Towards Explainable Traffic Flow Prediction with Large Language Models](https://arxiv.org/abs/2404.02937) | arXiv | 2024.04 | [Github](https://github.com/guoxs/xtp-llm) |
| [GPT4MTS: Prompt-Based Large Language Model for Multimodal Time-Series Forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/30383) | AAAI | 2024.03 | [Github](https://github.com/Flora-jia-jfr/GPT4MTS-Prompt-based-Large-Language-Model-for-Multimodal-Time-series-Forecasting) |
## Time Series Reasoning
| Title | Venue | Month | Code |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-------:|:-----------------------------------------------------------------------:|
| [MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering](https://arxiv.org/abs/2503.16858) | arXiv | 2025.03 | [Github](https://github.com/Graph-and-Geometric-Learning/MTBench) |
| [Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data](https://arxiv.org/abs/2503.10883) | arXiv | 2025.03 | None |
| [Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement](https://www.arxiv.org/abs/2503.01875) | arXiv | 2025.02 | [HuggingFace](https://huggingface.co/Time-QA) |
| [Position: Empowering Time Series Reasoning with Multimodal LLMs](https://arxiv.org/abs/2502.01477) | arXiv | 2025.02 | None |
| [ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data](https://arxiv.org/abs/2412.11376) | AAAI | 2024.12 | [Github](https://github.com/ForestsKing/ChatTime) |
| [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://arxiv.org/abs/2412.03104) | arXiv | 2024.12 | [Github](https://github.com/NetManAIOps/ChatTS) |
| [A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization](https://arxiv.org/abs/2411.06018) | arXiv | 2024.11 | [Github](https://github.com/haoxin1998/TimberBed-1) |
| [TimeSeriesExam: A Time Series Understanding Exam](https://arxiv.org/abs/2410.14752) | arXiv | 2024.10 | [HuggingFace](https://huggingface.co/datasets/AutonLab/TimeSeriesExam1) |
| [Can LLMs Understand Time Series Anomalies?](https://arxiv.org/abs/2410.05440) | ICLR | 2024.10 | [Github](https://github.com/Rose-STL-Lab/AnomLLM/) |
| [Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution](https://arxiv.org/abs/2410.04047) | arXiv | 2024.10 | None |
| [Towards Time Series Reasoning with LLMs](https://arxiv.org/abs/2409.11376) | arXiv | 2024.09 | None |
| [Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark](https://arxiv.org/abs/2404.16563) | arXiv | 2024.04 | None |
| [Language Models Still Struggle to Zero-shot Reason about Time Series](https://arxiv.org/abs/2404.11757) | arXiv | 2024.04 | [Github](https://github.com/behavioral-data/TSandLanguage) |
## Time Series Captioning
| Title | Venue | Month | Code |
|:------------------------------------------------------------------------------------------------------------------------|:-----:|:-------:|:----:|
| [Time Series Language Model for Descriptive Caption Generation](https://www.arxiv.org/abs/2501.01832) | arXiv | 2025.01 | None |
| [TADACap: Time-series Adaptive Domain-Aware Captioning](https://arxiv.org/abs/2504.11441) | ICAIF | 2024.12 | None |
| [Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation](https://arxiv.org/abs/2410.17462) | arXiv | 2024.10 | None |
| [Towards Time Series Reasoning with LLMs](https://arxiv.org/abs/2409.11376) | arXiv | 2024.09 | None |
## Time Series Alignment
| Title | Venue | Month | Code |
|:-----------------------------------------------------------------------------------------------------------------------|:-----:|:-------:|:----:|
| [CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision](https://arxiv.org/abs/2411.08397) | arXiv | 2024.11 | None |