Update README.md

Zero-coder · web-flow · commit 86fd41113462 · 2022-12-04T20:21:39.000-05:00
diff --git a/README.md b/README.md
@@ -1,5 +1,14 @@
 # FECAM(In VLDB2023 Submission)
 
+[![Arxiv link]()](https://arxiv.org/abs/2212.01209)
+<!-- 
+https://img.shields.io/badge/arXiv-Time%20Series%20is%20a%20Special%20Sequence%3A%20Forecasting%20with%20Sample%20Convolution%20and%20Interaction-%23B31B1B -->
+![state-of-the-art](https://img.shields.io/badge/-STATE--OF--THE--ART-blue?logo=Accenture&labelColor=lightgrey)![pytorch](https://img.shields.io/badge/-PyTorch-%23EE4C2C?logo=PyTorch&labelColor=lightgrey)
+
+
+
+This is the original pytorch implementation for the following paper: [FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting](# FECAM(In VLDB2023 Submission)
+
 [![Arxiv link]()](https://arxiv.org/pdf/)
 <!-- 
 https://img.shields.io/badge/arXiv-Time%20Series%20is%20a%20Special%20Sequence%3A%20Forecasting%20with%20Sample%20Convolution%20and%20Interaction-%23B31B1B -->
@@ -149,6 +158,197 @@ We conduct the experiments on **6** popular time-series datasets, namely **Elect
 
 
 
+### Dataset preparation
+Download data. You can obtain all the six benchmarks from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/) or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing). **All the datasets are well pre-processed** and can be used easily.(We thanks Author of Autoformer ,Haixu Wu for sorting datasets and public sharing them.)
+
+The data directory structure is shown as follows. 
+```
+./
+└── datasets/
+    ├── electricity
+    │   └── electricity.csv
+    ├── ETT-small
+    │   ├── ETTh1.csv
+    │   ├── ETTh2.csv
+    │   ├── ETTm1.csv
+    │   └── ETTm2.csv
+    ├── exchange_rate
+    │   └── exchange_rate.csv
+    ├── illness
+    │   └── national_illness.csv
+    ├── traffic
+    │   └── traffic.csv
+    └── weather
+        └── weather.csv
+```
+## Contact
+
+If you have any questions, feel free to contact us or post github issues. Pull requests are highly welcomed!
+
+```
+Maowei Jiang: jiangmaowei@sia.cn
+```
+
+
+## Acknowledgements
+
+Thank you all for your attention to our work!
+
+This code uses ([Autoformer](https://github.com/thuml/Autoformer),[Informer](https://github.com/zhouhaoyi/Informer2020), [Reformer](https://github.com/lucidrains/reformer-pytorch), [Transformer](https://github.com/jadore801120/attention-is-all-you-need-pytorch), [LSTM](https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction),[N-HiTS](https://github.com/Nixtla/neuralforecast), [N-BEATS](https://github.com/ServiceNow/N-BEATS), [Pyraformer](https://github.com/alipay/Pyraformer), [ARIMA](https://github.com/gmonaci/ARIMA)) as baseline methods for comparison and further improvement.
+
+We appreciate the following github repos a lot for their valuable code base or datasets:
+
+https://github.com/zhouhaoyi/Informer2020
+
+https://github.com/thuml/Autoformer
+
+https://github.com/cure-lab/LTSF-Linear
+
+https://github.com/zhouhaoyi/ETDataset
+
+https://github.com/laiguokun/multivariate-time-series-data
+). Alse see the [Open Review verision]().  
+
+If you find this repository useful for your research work, please consider citing it as follows:
+
+```
+
+@article{2022FECAM,
+
+title={FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting},
+
+author={Jiang, Maowei and Zeng, Pengyu and Wang, Kai and Chen, Wenbo and Liu, Huan and Liu, Haoran},
+
+journal=Arxiv, 2022},
+
+year={2022}
+
+}
+
+```
+
+## Updates
+- [2022-12-01] FECAM v1.0 is released
+
+
+## Features
+
+- [x] Support **Six** popular time-series forecasting datasets, namely Electricity Transformer Temperature (ETTh1, ETTh2 and ETTm1,ETTm2) , Traffic, National Illness, Electricity and Exchange Rate , ranging from power, energy, finance,illness and traffic domains.
+- [x] **We generalize FECAM into a module which can be flexibly and easily applied into any deep learning models within just few code lines.**
+
+[comment]: <> (![traffic]&#40;https://img.shields.io/badge/🚅-Traffic-yellow&#41;)
+
+[comment]: <> (![electric]&#40;https://img.shields.io/badge/%F0%9F%92%A1-Electricity-yellow&#41;)
+
+[comment]: <> (![Solar Energy]&#40;https://img.shields.io/badge/%F0%9F%94%86-Solar%20Energy-yellow&#41;)
+
+[comment]: <> (![finance]&#40;https://img.shields.io/badge/💵-Finance-yellow&#41;)
+
+- [x] Provide all training logs.
+
+
+
+## To-do items
+
+- Integrate FECAM into other mainstream models(eg:Pyraformer,Bi-lstm,etc.) for better performance and higher efficiency on real-world time series.
+- Validate FECAM on more spatial-temporal time series datasets.
+- As a sequence modelling module,we believe it can work fine on NLP tasks too,like Machine Translation and Name Entity Recognization.Further more,as a frequency enhanced module it can theoretically work in any deep-learning models like Resnet.  
+
+Stay tuned!
+
+## Get started
+
+1. Install the required package first(Mainly including Python 3.8, PyTorch 1.9.0):
+```
+    cd FECAM
+    conda create -n fecam python=3.8
+    conda activate fecam
+    pip install -r requirements.txt
+```
+2. Download data. You can obtain all the six benchmarks from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/) or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing). **All the datasets are well pre-processed** and can be used easily.
+3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`. You can reproduce the experiment results by:
+```
+    bash ./scripts/ETT_script/FECAM_ETTm2.sh
+    bash ./scripts/ECL_script/FECAM.sh
+    bash ./scripts/Exchange_script/FECAM.sh
+    bash ./scripts/Traffic_script/FECAM.sh
+    bash ./scripts/Weather_script/FECAM.sh
+    bash ./scripts/ILI_script/FECAM.sh
+```
+## SENET(channel attention)
+<p align="center">
+<img src=".\pics\SENET.png" height = "250" alt="" align=center />
+</p>
+
+## FECAM(Frequency Enhanced Channel Attention Mechanism)
+<p align="center">
+<img src=".\pics\FECAM.png" height = "350" alt="" align=center />
+</p>
+
+## As a module to enhance the frequency domain modeling capability of transformers and LSTM
+<p align="center">
+<img src=".\pics\as_module.png" height = "450" alt="" align=center />
+</p>
+
+## Comparison with Transformers and other mainstream forecasting models
+### Multivariate Forecasting:
+<p align="center">
+<img src=".\pics\mul.png" height = "550" alt="" align=center />
+</p>
+
+FECAM outperforms all transformer-based methods by a large margin.
+### Univariate Forecasting:
+<p align="center">
+<img src=".\pics\uni.png" height = "280" alt="" align=center />
+</p>
+
+### Efficiency
+<p align="center">
+<img src=".\pics\parameter_increment.png" height = "185" alt="" align=center />
+</p>
+Compared to vanilla models, only a few parameters are increased by applying our method (See Table 4), and thereby their computationalcomplexities can be preserved.
+
+
+### Performance promotion with FECAM module
+<p align="center">
+<img src=".\pics\performance_promotion.png" height = "390" alt="" align=center />
+</p>
+
+
+## Visualization
+### Forecasting visualization:Visualization of ETTm2 and Exchange predictions given by different models.
+<p align="center">
+<img src=".\pics\Qualitative_withours.png" height = "397" alt="" align=center />
+
+### FECAM visualization:Visualization of frequency enhanced channel attention and output tensor of encoder layer of transformer.x-axis represents channels,y-axis represents frequency from low to high,performing on datasets weather and exchange.
+<p align="center">
+<img src=".\pics\tensor_visualization.png" height = "345" alt="" align=center />
+
+## Used Datasets
+
+
+We conduct the experiments on **6** popular time-series datasets, namely **Electricity Transformer Temperature (ETTh1, ETTh2 and ETTm1) and Traffic, Weather,Illness, Electricity and Exchange Rate**, ranging from **power, energy, finance , health care and traffic domains**. 
+
+
+### Overall information of the 9 real world datasets
+
+| Datasets      | Variants | Timesteps | Granularity | Start time | Task Type   |
+| ------------- | -------- | --------- | ----------- | ---------- | ----------- |
+| ETTh1         | 7        | 17,420    | 1hour       | 7/1/2016   | Multi-step  |
+| ETTh2         | 7        | 17,420    | 1hour       | 7/1/2016   | Multi-step  |
+| ETTm1         | 7        | 69,680    | 15min       | 7/1/2016   | Multi-step  |
+| ETTm2         | 7        | 69,680    | 15min       | 7/1/2016   | Multi-step&Single-step  |
+| ILI           | 7        | 966       | 1hour       | 1/1/2002   | Multi-step  |
+| Exchange-Rate | 8        | 7,588     | 1hour       | 1/1/1990   | Multi-step&Single-step |
+| Electricity   | 321      | 26,304    | 1hour       | 1/1/2012   | Multi-step-step |
+| Traffic       | 862      | 17,544    | 1hour       | 1/1/2015   | Multi-step-step |
+| Weather       | 21       | 52,695    | 10min       | 1/1/2020   | Multi-step-step |
+
+
+
+
+
+
 ### Dataset preparation
 Download data. You can obtain all the six benchmarks from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/) or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing). **All the datasets are well pre-processed** and can be used easily.(We thanks Author of Autoformer ,Haixu Wu for sorting datasets and public sharing them.)
 
