# 
SymTime NeurIPS 2025 
This code is the official PyTorch implementation of our NeurIPS'25 paper: **Synthetic Series-Symbol Data Generation for Time Series Foundation Models**.
[](https://neurips.cc/virtual/2025/poster/115260) [](https://pypi.org/project/s2generator/) [](https://www.python.org/) [](https://pytorch.org/)
[Paper](https://arxiv.org/abs/2510.08445) | [Poster](https://github.com/wwhenxuan/wwhenxuan.github.io/blob/main/assets/img/poster_neurips_2025_115260_synthetic_series-symbol_data_generation.jpg) | [Blog](https://mp.weixin.qq.com/s/D6O5SBl2RYHdkiinV6UM8w) | [Video](https://www.bilibili.com/video/BV1RT4QzXECt/?spm_id_from=333.337.search-card.all.click) | [PPT](https://github.com/wwhenxuan/wwhenxuan.github.io/blob/main/assets/files/NeurIPS_2025_SymTime_video_en.pptx) | [Citation](#Citation)
## ✨ Introduction
Due to issues such as data privacy and acquisition difficulties, existing large-scale time series datasets face severe data shortages and imbalanced distribution compared to images and natural language. Foundation models pre-trained on these datasets will have certain prediction biases, reducing their generalization and robustness.
Inspired by complex dynamic system theories, we design a [series-symbol](https://github.com/wwhenxuan/S2Generator) data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic expressions. To leverage series-symbol data pairs with strong correlations, we develop **SymTime**, a pre-trained foundation model for enhancing time series representation using symbolic information, which demonstrates competitive performance across five major TSA tasks, rivaling foundation models pre-trained on real-world datasets.
## 🧭 Quickstart
### Installation
First create a Python virtual environment (preferably version 3.10.15), then install the required dependencies by running the following command:
```
pip install -r requirements.txt
```
### Data Preparation 
SymTime relies on a large-scale series-symbol bimodal dataset generated by [`S2Generator`](https://github.com/wwhenxuan/S2Generator) during pre-training. You can generate the data required for pre-training by executing the following script:
```bash
bash ./scripts/s2generator.sh
```
For the fine-tuning datasets, you can load from [OneDrive](https://drive.google.com/drive/folders/1of5P-Cy-dve9zs09p_Gr_wHh8Z_hfRN_?usp=sharing) or [BaiduCloud](https://pan.baidu.com/s/1gj44jULMdtCBLC_BwRrqVA?pwd=6666). Then place the downloaded datasets under the folder `./datasets`.
### Model Pre-Train and Fine-Tune
Once you have generated enough time series data using [`S2Generator`](https://github.com/wwhenxuan/S2Generator), you can pre-train SymTime by executing our pre-training script:
```shell
bash ./scripts/SymTime_pretrain.sh
```
If you want to skip the time-consuming pre-training phase, you can directly download our pre-trained model parameters from [OneDrive](https://drive.google.com/drive/folders/1of5P-Cy-dve9zs09p_Gr_wHh8Z_hfRN_?usp=sharing) or [BaiduCloud](https://pan.baidu.com/s/1gj44jULMdtCBLC_BwRrqVA?pwd=6666) and put them under `./models/params/` for fine-tuning on downstream tasks:
```shell
# Long-term time series forecasting
bash ./scripts/long_term_forecasting/SymTime_ECL.sh
# Short-term time series forecasting
bash ./scripts/short_term_forecasting/SymTime_M4.sh
# Time series classification
bash ./scripts/classification/SymTime_EthanolConcentration.sh
# Time series imputation
bash ./scripts/imputation/SymTime_ECL.sh
# Time series anomaly detection
bash ./scripts/anomaly_detection/MSL.sh
```
## 📊 Results
### Main Results
Compared with other models for general time series analysis tasks, **SymTime**, which has been pre-trained with mask modeling and cross-modal contrastive learning, can achieve SOTA results in fine-tuning of downstream tasks and has lower model complexity.
### Benchmark Results
We present the experimental results on the `TimesNet` benchmark. Compared with the current more advanced models, **SymTime** can achieve better experimental results.
### Dataset and Representation Learning
We generate a large amount of series-symbol bimodal data using [`S2Generator`](https://github.com/wwhenxuan/S2Generator) for pre-training of mask modeling and contrastive learning. Therefore, we first verify the representation coverage of the synthetic data compared with real-world time series dataset.
Then, we visualize the representation space of time series encoder (a)(b) and symbolic expression encoder (c)(d) in **SymTime** before and after pre-training. The paired time series and symbolic expressions form distinct clustering features, demonstrating the effectiveness of our pre-training paradigm.
## 🎓 Citation
If you find this code useful, please cite our paper.
```
@misc{wang2025syntheticseriessymboldatageneration,
title={Synthetic Series-Symbol Data Generation for Time Series Foundation Models},
author={Wenxuan Wang and Kai Wu and Yujian Betterest Li and Dan Wang and Xiaoyu Zhang},
year={2025},
eprint={2510.08445},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2510.08445},
}
```
## 🎖️ Acknowledgement
We appreciate the following GitHub repos a lot for their valuable code and efforts.
- Time-Series-Library (https://github.com/thuml/Time-Series-Library);
- PySDKit (https://github.com/wwhenxuan/PySDKit);
- ALBEF (https://github.com/salesforce/ALBEF);
- PatchTST (https://github.com/yuqinie98/PatchTST);
- Short-term Forecasting: (https://github.com/ServiceNow/N-BEATS).
## 🤗 Contact
If you have any questions or are interested in our view on the complex dynamics of time series, feel free to contact:
- [Whenxuan Wang](https://wwhenxuan.github.io/) (whenxuanwang@stu.xidian.edu.cn)
- [Kai Wu](https://sparsel.github.io/index.html) (kwu@xidian.edu.cn)
- [Dan Wang](https://web.xidian.edu.cn/danwang/) (danwang@xidian.edu.cn)
