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 # ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
-<img src="pics/etsformer.png" width="700">
+
+<p align="center">
+<img src=".\pics\etsformer.png" width = "700" alt="" align=center />
+<br><br>
+<b>Figure 1.</b> Overall ETSformer Architecture.
+</p>
 
 Official PyTorch code repository for the [ETSformer paper](https://arxiv.org/abs/2202.01381).
 
+* ETSformer is a novel time-series Transformer architecture which exploits the principle of exponential smoothing in improving
+Transformers for timeseries forecasting.
+* ETSformer is inspired by the classical exponential smoothing methods in
+time-series forecasting, leveraging the novel exponential smoothing attention (ESA) and frequency attention (FA) to
+replace the self-attention mechanism in vanilla Transformers, thus improving both accuracy and efficiency.
+
 ## Requirements
-Required dependencies can be installed by:
-```bash
-pip install -r requirements.txt
-```
+
+1. Install Python 3.8, and the required dependencies.
+2. Required dependencies can be installed by: ```pip install -r requirements.txt```
 
 ## Data
-* Pre-processed datasets can be downloaded from the following links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/) or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained from [Autoformer's](https://github.com/thuml/Autoformer) GitHub repository. 
+
+* Pre-processed datasets can be downloaded from the following
+  links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/)
+  or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained
+  from [Autoformer's](https://github.com/thuml/Autoformer) GitHub repository.
 * Place the downloaded datasets into the `dataset/` folder, e.g. `dataset/ETT-small/ETTm2.csv`.
 
 ## Usage
 
-1. Install Python 3.8, and the required dependencies.
+1. Install the required dependencies.
 2. Download data as above, and place them in the folder, `dataset/`.
-3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`, e.g. `./scripts/ETTm2.sh`. You might have to change permissions on the script files by running`chmod u+x scripts/*`.
+3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`,
+   e.g. `./scripts/ETTm2.sh`. You might have to change permissions on the script files by running`chmod u+x scripts/*`.
 4. The script for grid search is also provided, and can be run by `./grid_search.sh`.
 
+## Main Results
+
 ## Acknowledgements
-The implementation of ETSformer relies on resources from the following codebases and repositories, we thank the original authors for open-sourcing their work.
+
+The implementation of ETSformer relies on resources from the following codebases and repositories, we thank the original
+authors for open-sourcing their work.
+
 * https://github.com/thuml/Autoformer
 * https://github.com/zhouhaoyi/Informer2020
 
 ## Citation
+
 Please consider citing if you find this code useful to your research.
 <pre>@article{woo2022etsformer,
     title={ETSformer: Exponential Smoothing Transformers for Time-series Forecasting},