initial commit

LICENSE

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+MIT License
+
+Copyright (c) 2023 Temporal GNN
+
+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.
+

MTGNN/.gitignore

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+.idea
+__pycache__/
+.vscode/
+model*/
+prediction*/
+figs*/
+runs*/
+old_simulations/
+.DS_Store
+**/*experiment_log*.txt

MTGNN/LICENSE

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+MIT License
+
+Copyright (c) 2020 Zonghan Wu
+
+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.

MTGNN/README.md

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+This is forked and adapted for our studies from [MTGNN](https://github.com/nnzhan/MTGNN.git).
+
+# MTGNN
+This is a PyTorch implementation of the paper: [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://arxiv.org/abs/2005.11650), published in KDD-2020.
+
+## Requirements
+The model is implemented using Python3 with dependencies specified in requirements.txt
+## Data Preparation
+### Multivariate time series datasets
+
+Download Solar-Energy, Traffic, Electricity, Exchange-rate datasets from [https://github.com/laiguokun/multivariate-time-series-data](https://github.com/laiguokun/multivariate-time-series-data). Uncompress them and move them to the data folder.
+
+### Traffic datasets
+Download the METR-LA and PEMS-BAY dataset from [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g) provided by [Li et al.](https://github.com/liyaguang/DCRNN.git) . Move them into the data folder. 
+
+```
+
+# Create data directories
+mkdir -p data/{METR-LA,PEMS-BAY}
+
+# METR-LA
+python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
+
+# PEMS-BAY
+python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
+
+```
+
+## Model Training
+
+### Single-step
+
+* Solar-Energy
+
+```
+python train_single_step.py --save ./model-solar-3.pt --data ./data/solar_AL.txt --num_nodes 137 --batch_size 4 --epochs 30 --horizon 3
+#sampling
+python train_single_step.py --num_split 3 --save ./model-solar-sampling-3.pt --data ./data/solar_AL.txt --num_nodes 137 --batch_size 16 --epochs 30 --horizon 3
+```
+* Traffic 
+
+```
+python train_single_step.py --save ./model-traffic3.pt --data ./data/traffic.txt --num_nodes 862 --batch_size 16 --epochs 30 --horizon 3
+#sampling
+python train_single_step.py --num_split 3 --save ./model-traffic-sampling-3.pt --data ./data/traffic --num_nodes 321 --batch_size 16 --epochs 30 --horizon 3
+```
+
+* Electricity
+
+```
+python train_single_step.py --save ./model-electricity-3.pt --data ./data/electricity.txt --num_nodes 321 --batch_size 4 --epochs 30 --horizon 3
+#sampling 
+python train_single_step.py --num_split 3 --save ./model-electricity-sampling-3.pt --data ./data/electricity.txt --num_nodes 321 --batch_size 16 --epochs 30 --horizon 3
+```
+
+* Exchange-Rate
+
+```
+python train_single_step.py --save ./model/model-exchange-3.pt --data ./data/exchange_rate.txt --num_nodes 8 --subgraph_size 8  --batch_size 4 --epochs 30 --horizon 3
+#sampling
+python train_single_step.py --num_split 3 --save ./model-exchange-3.pt --data ./data/exchange_rate.txt --num_nodes 8 --subgraph_size 2  --batch_size 16 --epochs 30 --horizon 3
+```
+### Multi-step
+* METR-LA
+
+```
+python train_multi_step.py --adj_data ./data/sensor_graph/adj_mx.pkl --data ./data/METR-LA --num_nodes 207
+```
+* PEMS-BAY
+
+```
+python train_multi_step.py --adj_data ./data/sensor_graph/adj_mx_bay.pkl --data ./data/PEMS-BAY/ --num_nodes 325
+```
+
+## Citation
+
+```
+@inproceedings{wu2020connecting,
+  title={Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks},
+  author={Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Chang, Xiaojun and Zhang, Chengqi},
+  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
+  year={2020}
+}
+```

MTGNN/constants.py

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+from datetime import datetime
+
+from util import format_time_as_YYYYMMddHHmm
+
+
+class Constants:
+    PREDICTION_TIME = format_time_as_YYYYMMddHHmm(datetime.now())