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.
This repository contains the official Hugging Face / PyTorch implementation of SymTime from our NeurIPS 2025 paper, Synthetic Series-Symbol Data Generation for Time Series Foundation Models.
Overview
SymTime is a lightweight time series foundation model designed to learn strong temporal representations from patch-based inputs. It is built for practical downstream use and supports easy loading through the Hugging Face AutoModel interface.
The model takes a univariate time series, splits it into patches, and encodes the patch sequence with a transformer backbone. The repository includes the configuration, model definition, and a runnable example for inference.
Quick start
Install dependencies
pip install -r requirements.txt
Load the model
from transformers import AutoModel
model = AutoModel.from_pretrained("FlowVortex/SymTime", trust_remote_code=True)
Run inference
import torch
x = torch.randn(16, 256)
out = model(x)
out_no_cls = model(x, return_cls_token=False)
Model summary
- Input:
Tensorwith shape[batch_size, seq_length] - Output: patch embeddings, optionally with a CLS token output
- Backend: patch-based transformer encoder
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},
}
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 (whenxuanwang@stu.xidian.edu.cn)
- Kai Wu (kwu@xidian.edu.cn)
- Dan Wang (danwang@xidian.edu.cn)
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)
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