Using a vision MAE for time series forecasting.
Project description
🔍 About | 🚀 Quick Start | 📊 Evaluation | 🔗 Citation
🔍 About
-
We propose VisionTS, a time series forecasting (TSF) foundation model building from rich, high-quality natural images 🖼️.
- This is conceptually different from the existing TSF foundation models (text-based 📝 or time series-based 📈), but it shows a comparable or even better performance without any adaptation on time series data.
- We reformulate the TSF task as an image reconstruction task, which is further processed by a visual masked autoencoder (MAE).
🚀 Quick Start
We have uploaded our package to PyPI. Please first install pytorch, then running the following command for installing VisionTS:
pip install visionts
Then, you can refer to demo.ipynb about forecasting time series using VisionTS, with a clear visualization of the image reconstruction.
📊 Evaluation
Our repository is built on Time-Series-Library, MAE, and GluonTS. Please install the dependencies through requirements.txt
before running the evaluation.
Long-Term TSF Benchmarks (Zero-Shot)
We evaluate our methods on 6 long-term TSF benchmarks for zero-shot forecasting. The scripts are under long_term_tsf/scripts/vision_ts_zeroshot
. Before running, you should first follow the instructions of Time-Series-Library to download datasets into long_term_tsf/dataset
. Using the following command for reproduction:
cd long_term_tsf/
bash scripts/vision_ts_zeroshot/$SOME_DATASET.sh
Monash (Zero-Shot)
We evaluate our methods on 29 Monash TSF benchmarks. You can use the following command for reproduction, where the benchmarks will be automatically downloaded.
cd monash/
python run.py
Long-Term TSF Benchmarks (Full-Shot)
We evaluate our methods on 8 long-term TSF benchmarks for full-shot forecasting. The scripts are under long_term_tsf/scripts/vision_ts_fullshot
. Using the following command for reproduction:
cd long_term_tsf/
bash scripts/vision_ts_fullshot/$SOME_DATASET.sh
🔗 Citation
@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},
}
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