The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)".

conda create -n tempo python=3.8

conda activate tempo

pip install -r requirements.txt

Download the data from [Google Drive] or [Baidu Drive], and place the downloaded data in the folder./dataset. You can also download the STL results from [Google Drive], and place the downloaded data in the folder./stl.

bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather].sh

After training, we can test TEMPO model under the zero-shot setting:

bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather]_test.sh

You can download the pre-trained model from [Google Drive] and then run the test script for fun.

Here is the prompts use to generate the coresponding textual informaton of time series via [OPENAI ChatGPT-3.5 API]

The time series data are come from [S&P 500]. Here is the EBITDA case for one company from the dataset:

Example of generated contextual information for the Company marked above:

You can download the processed data with text embedding from GPT2 from: [TETS].

@inproceedings{
cao2024tempo,
title={{TEMPO}: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting},
author={Defu Cao and Furong Jia and Sercan O Arik and Tomas Pfister and Yixiang Zheng and Wen Ye and Yan Liu},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=YH5w12OUuU}
}