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Add the demos

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  1. README.md +7 -6
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@@ -23,7 +23,7 @@ base_model:
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  [![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/Melady/TEMPO)
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  [![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
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- <div align="center"><img src=./pics/TEMPO_logo.png width=60% /></div>
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  The official model card for ICLR 2024 paper: "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)".
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@@ -31,7 +31,8 @@ The official code for [["TEMPO: Prompt-based Generative Pre-trained Transformer
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  TEMPO is one of the very first open source **Time Series Foundation Models** for forecasting task v1.0 version.
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- <div align="center"><img src=./pics/TEMPO.png width=80% /></div>
 
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  ## 💡 Demos
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  bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather]_test.sh
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  ```
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- <div align="center"><img src=./pics/results.jpg width=90% /></div>
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  ## Pre-trained Models
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  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)
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- <div align="center"><img src=./pics/TETS_prompt.png width=80% /></div>
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  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:
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- <div align="center"><img src=./pics/Company1_ebitda_summary.png width=80% /></div>
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  Example of generated contextual information for the Company marked above:
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- <div align="center"><img src=./pics/Company1_ebitda_summary_words.jpg width=80% /></div>
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  [![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/Melady/TEMPO)
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  [![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
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+ ![TEMPO_logo](pics/TEMPO_logo.png)
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  The official model card for ICLR 2024 paper: "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)".
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  TEMPO is one of the very first open source **Time Series Foundation Models** for forecasting task v1.0 version.
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+ ![TEMPO-architecture](pics/TEMPO.png)
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+
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  ## 💡 Demos
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  bash [ecl, etth1, etth2, ettm1, ettm2, traffic, weather]_test.sh
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  ```
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+ ![TEMPO-results](pics/results.jpg)
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  ## Pre-trained Models
 
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  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)
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+ ![TEMPO-prompt](pics/TETS_prompt.png)
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  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:
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+ ![Company1_ebitda_summary](pics/Company1_ebitda_summary.png)
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  Example of generated contextual information for the Company marked above:
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+ ![Company1_ebitda_summary_words.jpg](pics/Company1_ebitda_summary_words.jpg.png)
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