TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
The official code for ["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.
π‘ Demos
1. Reproducing zero-shot experiments on ETTh2:
Please try to reproduc the zero-shot experiments on ETTh2 [here on Colab].
2. Zero-shot experiments on customer dataset:
We use the following Colab page to show the demo of building the customer dataset and directly do the inference via our pre-trained foundation model: [Colab]
π§ Hands-on: Using Foundation Model
1. Download the repo
git clone [email protected]:DC-research/TEMPO.git
2. [Optional] Download the model and config file via commands
huggingface-cli download Melady/TEMPO config.json --local-dir ./TEMPO/TEMPO_checkpoints
huggingface-cli download Melady/TEMPO TEMPO-80M_v1.pth --local-dir ./TEMPO/TEMPO_checkpoints
huggingface-cli download Melady/TEMPO TEMPO-80M_v2.pth --local-dir ./TEMPO/TEMPO_checkpoints
3. Build the environment
conda create -n tempo python=3.8
conda activate tempo
cd TEMPO
pip install -r requirements.txt
4. Script Demo
A streamlining example showing how to perform forecasting using TEMPO:
# Third-party library imports
import numpy as np
import torch
from numpy.random import choice
# Local imports
from models.TEMPO import TEMPO
model = TEMPO.load_pretrained_model(
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'),
repo_id = "Melady/TEMPO",
filename = "TEMPO-80M_v1.pth",
cache_dir = "./checkpoints/TEMPO_checkpoints"
)
input_data = np.random.rand(336) # Random input data
with torch.no_grad():
predicted_values = model.predict(input_data, pred_length=96)
print("Predicted values:")
print(predicted_values)
5. Online demo
Please try our foundation model demo [here].
π¨ Advanced Practice: Full Training Workflow!
We also updated our models on HuggingFace: [Melady/TEMPO].
1. 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
.
2. Run Scripts
2.1 Pre-Training Stage
bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather].sh
2.2 Test/ Inference Stage
After training, we can test TEMPO model under the zero-shot setting:
bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather]_test.sh
Pre-trained Models
You can download the pre-trained model from [Google Drive] and then run the test script for fun.
TETS dataset
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].
π News
Oct 2024: π We've streamlined our code structure, enabling users to download the pre-trained model and perform zero-shot inference with a single line of code! Check out our demo for more details. Our model's download count on HuggingFace is now trackable!
Jun 2024: π We added demos for reproducing zero-shot experiments in Colab. We also added the demo of building the customer dataset and directly do the inference via our pre-trained foundation model: Colab
May 2024: π TEMPO has launched a GUI-based online demo, allowing users to directly interact with our foundation model!
May 2024: π TEMPO published the 80M pretrained foundation model in HuggingFace!
May 2024: π§ͺ We added the code for pretraining and inference TEMPO models. You can find a pre-training script demo in this folder. We also added a script for the inference demo.
Mar 2024: π Released TETS dataset from S&P 500 used in multimodal experiments in TEMPO.
Mar 2024: π§ͺ TEMPO published the project code and the pre-trained checkpoint online!
Jan 2024: π TEMPO paper get accepted by ICLR!
Oct 2023: π TEMPO paper released on Arxiv!
β³ Upcoming Features
- [β ] Parallel pre-training pipeline
- [] Probabilistic forecasting
- [] Multimodal dataset
- [] Multimodal pre-training script
Contact
Feel free to connect [email protected] / [email protected] if youβre interested in applying TEMPO to your real-world application.
Cite our work
@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}
}
@article{
Jia_Wang_Zheng_Cao_Liu_2024,
title={GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting},
volume={38},
url={https://ojs.aaai.org/index.php/AAAI/article/view/30383},
DOI={10.1609/aaai.v38i21.30383},
number={21},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Jia, Furong and Wang, Kevin and Zheng, Yixiang and Cao, Defu and Liu, Yan},
year={2024}, month={Mar.}, pages={23343-23351}
}
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