update README.md

README.md

@@ -11,6 +11,69 @@ app_file: app.py
 pinned: false
 license: mit
 short_description: space for VisionTSpp
+pipeline_tag: time-series-forecasting
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Visual Backbones
+
+This repository hosts the **VisionTS++** model, a state-of-the-art time series foundation model based on continual pre-training of a visual Masked AutoEncoder (MAE) on large-scale time series data. It excels in multivariate and probabilistic time series forecasting by bridging modality gaps between vision and time series data.
+
+The model was introduced in the paper:
+[**VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones**](https://arxiv.org/abs/2508.04379)
+
+Official GitHub repository: [https://github.com/HALF111/VisionTSpp](https://github.com/HALF111/VisionTSpp)
+
+Experience **VisionTS++** directly in your browser on this [Hugging Face Space](https://huggingface.co/spaces/Lefei/VisionTSpp)! You can upload your own custom time series CSV file for zero-shot forecasting.
+
+## About
+VisionTS++ is built upon continual pre-training of a vision model on large-scale time series, addressing key discrepancies in cross-modal transfer from vision to time series. It introduces three key innovations:
+
+1.  **Vision-model-based filtering**: Identifies high-quality sequences to stabilize pre-training and mitigate the data-modality gap.
+2.  **Colorized multivariate conversion**: Encodes multivariate series as multi-subfigure RGB images to enhance cross-variate modeling.
+3.  **Multi-quantile forecasting**: Uses parallel reconstruction heads to generate quantile forecasts for probabilistic predictions without parametric assumptions.
+
+These innovations allow VisionTS++ to achieve state-of-the-art performance in both in-distribution and out-of-distribution forecasting, demonstrating that vision models can effectively generalize to Time Series Forecasting with appropriate adaptation.
+
+
+## Installation
+
+The VisionTS++ model is available through the `visionts` package on PyPI.
+
+First, install the package:
+
+```shell
+pip install visionts
+```
+
+If you want to develop the inference code, you can also build from source:
+
+```shell
+git clone https://github.com/HALF111/VisionTSpp.git
+cd VisionTSpp
+pip install -e .
+```
+
+For detailed inference examples and usage with clear visualizations of image reconstruction, please refer to the `demo.ipynb` notebook in the [official GitHub repository](https://github.com/HALF111/VisionTSpp/blob/main/demo.ipynb).
+
+## Citation
+If you're using VisionTS++ or VisionTS in your research or applications, please cite them using this BibTeX:
+
+```bibtex
+@misc{chen2024visionts,
+      title={VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters}, 
+      author={Mouxiang Chen and Lefei Shen and Zhuo Li and Xiaoyun Joy Wang and Jianling Sun and Chenghao Liu},
+      year={2024},
+      eprint={2408.17253},
+      archivePrefix={arXiv},
+      url={https://arxiv.org/abs/2408.17253}, 
+}
+@misc{shen2025visiontspp,
+      title={VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Visual Backbones}, 
+      author={Lefei Shen and Mouxiang Chen and Xu Liu and Han Fu and Xiaoxue Ren and Jianling Sun and Zhuo Li and Chenghao Liu},
+      year={2025},
+      eprint={2508.04379},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2508.04379}, 
+}
+```