Add the demos

README.md

@@ -16,20 +16,75 @@ tags:
 ---
 # 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)".
+[![preprint](https://img.shields.io/static/v1?label=arXiv&message=2310.04948&color=B31B1B&logo=arXiv)](https://arxiv.org/pdf/2310.04948)
+[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/Melady/TEMPO)
+[![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+
+<div align="center"><img src=./pics/TEMPO_logo.png width=60% /></div>
+
+The official model card for ICLR 2024 paper: "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)".
+
+The official code for [["TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)"]](https://arxiv.org/pdf/2310.04948).
 
 TEMPO is one of the very first open source **Time Series Foundation Models** for forecasting task v1.0 version.
 
-![TEMPO-architecture](pics/TEMPO.png)
+<div align="center"><img src=./pics/TEMPO.png width=80% /></div>
 
+## 💡 Demos
 
+### 1. Reproducing zero-shot experiments on ETTh2:
 
-Please try our foundation model demo [[here]](https://4171a8a7484b3e9148.gradio.live).
+Please try to reproduc the zero-shot experiments on ETTh2 [[here on Colab]](https://colab.research.google.com/drive/11qGpT7H1JMaTlMlm9WtHFZ3_cJz7p-og?usp=sharing).
+
+### 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]](https://colab.research.google.com/drive/1ZpWbK0L6mq1pav2yDqOuORo4rHbv80-A?usp=sharing)
+
+
+## ⏳ Upcoming Features
+
+- [✅] Parallel pre-training pipeline
+- [] Probabilistic forecasting
+- [] Multimodal dataset
+- [] Multimodal pre-training script
+
+## 🚀 News
+
+
+- **Oct 2024**: 🚀 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](./run_TEMPO_demo.py) for more details. Our model's download count on HuggingFace is now trackable!
+
+- **Jun 2024**: 🚀 We added demos for reproducing zero-shot experiments in [Colab](https://colab.research.google.com/drive/11qGpT7H1JMaTlMlm9WtHFZ3_cJz7p-og?usp=sharing).  We also added the demo of building the customer dataset and directly do the inference via our pre-trained foundation model: [Colab](https://colab.research.google.com/drive/1ZpWbK0L6mq1pav2yDqOuORo4rHbv80-A?usp=sharing)
+- **May 2024**: 🚀 TEMPO has launched a GUI-based online [demo](https://4171a8a7484b3e9148.gradio.live/), allowing users to directly interact with our foundation model!
+- **May 2024**: 🚀 TEMPO published the 80M pretrained foundation model in [HuggingFace](https://huggingface.co/Melady/TEMPO)!
+- **May 2024**: 🧪 We added the code for pretraining and inference TEMPO models.  You can find a pre-training script demo in [this folder](./scripts/etth2.sh). We also added [a script](./scripts/etth2_test.sh) for the inference demo.
+
+- **Mar 2024**: 📈  Released [TETS dataset](https://drive.google.com/file/d/1Hu2KFj0kp4kIIpjbss2ciLCV_KiBreoJ/view?usp=drive_link) from [S&P 500](https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview) used in multimodal experiments in TEMPO. 
+- **Mar 2024**: 🧪 TEMPO published the project [code](https://github.com/DC-research/TEMPO) and the pre-trained checkpoint [online](https://drive.google.com/file/d/11Ho_seP9NGh-lQCyBkvQhAQFy_3XVwKp/view?usp=drive_link)! 
+- **Jan 2024**: 🚀 TEMPO [paper](https://openreview.net/pdf?id=YH5w12OUuU) get accepted by ICLR!
+- **Oct 2023**: 🚀 TEMPO [paper](https://arxiv.org/pdf/2310.04948) released on Arxiv!
 
-![TEMPO-demo](pics/TEMPO_demo.jpg)
 
+# Practice
 
-# Build the environment
+## Download the repo
+
+```
+git clone git@github.com:DC-research/TEMPO.git
+```
+
+## [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_v2.pth --local-dir ./TEMPO/TEMPO_checkpoints
+```
+
+```
+!huggingface-cli download Melady/TEMPO TEMPO-80M_v1.pth --local-dir ./TEMPO/TEMPO_checkpoints
+```
+
+## Build the environment
 
 ```
 conda create -n tempo python=3.8
@@ -38,50 +93,93 @@ conda create -n tempo python=3.8
 conda activate tempo
 ```
 ```
+cd TEMPO
+```
+```
 pip install -r requirements.txt
 ```
 
-# Get Data
+## Script Demo
+
+A streamlining example showing how to perform forecasting using TEMPO:
+
+```python
+# 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)
+
+```
+
+
+## Online demo:
+
+Please try our foundation model demo [[here]](https://4171a8a7484b3e9148.gradio.live).
+
+<div align="center"><img src=./pics/TEMPO_demo.jpg width=80% /></div>
+
+## Practice on your end
+
+We also updated our models on HuggingFace: [[Melady/TEMPO]](https://huggingface.co/Melady/TEMPO).
+
+
+
+### Get Data
 
    Download the data from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing) or [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy), and place the downloaded data in the folder`./dataset`. You can also download the STL results from [[Google Drive]](https://drive.google.com/file/d/1gWliIGDDSi2itUAvYaRgACru18j753Kw/view?usp=sharing), and place the downloaded data in the folder`./stl`.
 
-# Run TEMPO
+### Run TEMPO
 
-## Training Stage
+### Pre-Training Stage
 ```
 bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather].sh
 ```
 
-## Test
+### 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
 ```
-![TEMPO-results](pics/results.jpg)
+
+<div align="center"><img src=./pics/results.jpg width=90% /></div>
 
 
-# Pre-trained Models
+## Pre-trained Models
 
 You can download the pre-trained model from [[Google Drive]](https://drive.google.com/file/d/11Ho_seP9NGh-lQCyBkvQhAQFy_3XVwKp/view?usp=drive_link) and then run the test script for fun.
 
-# Multi-modality dataset: TETS dataset
+## TETS dataset
 
 Here is the prompts use to generate the coresponding textual informaton of time series via [[OPENAI ChatGPT-3.5 API]](https://platform.openai.com/docs/guides/text-generation)
 
-![TEMPO-prompt](pics/TETS_prompt.png)
-
+<div align="center"><img src=./pics/TETS_prompt.png width=80% /></div>
 
 The time series data are come from [[S&P 500]](https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview). Here is the EBITDA case for one company from the dataset:
 
-![Company1_ebitda_summary](pics/Company1_ebitda_summary.png)
 
+<div align="center"><img src=./pics/Company1_ebitda_summary.png width=80% /></div>
 
 Example of generated contextual information for the Company marked above:
 
-![Company1_ebitda_summary_words.jpg](pics/Company1_ebitda_summary_words.jpg)
-
+<div align="center"><img src=./pics/Company1_ebitda_summary_words.jpg width=80% /></div>
 
 
 
@@ -89,9 +187,10 @@ Example of generated contextual information for the Company marked above:
 You can download the processed data with text embedding from GPT2 from: [[TETS]](https://drive.google.com/file/d/1Hu2KFj0kp4kIIpjbss2ciLCV_KiBreoJ/view?usp=drive_link
 ).
 
+## Contact
+Feel free to connect DefuCao@USC.EDU / YanLiu.CS@USC.EDU if you’re interested in applying TEMPO to your real-world application.
 
-
-## Cite
+## Cite our work
 ```
 @inproceedings{
 cao2024tempo,
@@ -101,4 +200,18 @@ 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} 
+   }
+```