add readme and licence

Author: MAZiqing

LICENSE

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+MIT License
+
+Copyright (c) 2021 xxxx
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# FEDformer (ICML 2022 under review)
+
+
+Frequency Enhanced Decomposed
+Transformer (FEDformer) is more efficient than
+standard Transformer with a linear complexity
+to the sequence length. 
+
+Our empirical studies
+with six benchmark datasets show that compared
+with state-of-the-art methods, FEDformer can
+reduce prediction error by 14.8% and 22.6%
+for multivariate and univariate time series,
+respectively.
+
+
+## Get Started
+
+1. Install Python 3.6, PyTorch 1.9.0.
+2. Download data. You can obtain all the six benchmarks from xxxx.
+3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`. You can reproduce the experiment results by:
+
+```bash
+bash ./scripts/run_M.sh
+bash ./scripts/run_S.sh
+```
+
+
+## Citation
+
+If you find this repo useful, please cite our paper. 
+
+```
+xxxxx
+```
+
+## Contact
+
+If you have any question or want to use the code, please contact xxx@xxxx .
+
+## Acknowledgement
+
+We appreciate the following github repos a lot for their valuable code base or datasets:
+
+https://github.com/thuml/Autoformer
+
+https://github.com/zhouhaoyi/Informer2020
+
+https://github.com/zhouhaoyi/ETDataset
+
+https://github.com/laiguokun/multivariate-time-series-data
+

scripts/run_M.sh

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 export CUDA_VISIBLE_DEVICES=1
 
-cd ..
+#cd ..
 
 for model in FEDformer Autoformer Informer Transformer
 do

scripts/run_S.sh

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 export CUDA_VISIBLE_DEVICES=0
 
-cd ..
+#cd ..
 
 for model in FEDformer Autoformer Informer Transformer
 do