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metadata
license: apache-2.0
datasets:
  - ETDataset/ett
language:
  - en
metrics:
  - mse
  - mae
library_name: transformers
pipeline_tag: time-series-forecasting
tags:
  - Time-series
  - foundation-model
  - forecasting

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

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

TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

TEMPO-architecture

Please try our foundation model demo [here].

TEMPO-demo

Build the environment

conda create -n tempo python=3.8
conda activate tempo
pip install -r requirements.txt

Get Data

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.

Run TEMPO

Training Stage

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

Test

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

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

TEMPO-results

Pre-trained Models

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

Multi-modality dataset: TETS dataset

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

TEMPO-prompt

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

Company1_ebitda_summary

Example of generated contextual information for the Company marked above:

Company1_ebitda_summary_words.jpg

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

Cite

@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}
}