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View Code? Open in Web Editor NEWCode repository for research project on a comparative study using Transformer, Statistical, Machine and Deep Learning methods for forecasting pollution trends
Code repository for research project on a comparative study using Transformer, Statistical, Machine and Deep Learning methods for forecasting pollution trends
# PolTrans-Comparative-Study ## Install $ python3 -m venv venv $ source venv/bin/activate $ pip install -r mlenv-packages.txt ## Run Jupyter Notebooks $ jupyter lab ## Directory Structure ├── 00A-Data Exploration.ipynb ├── 00B-Data Figure.ipynb ├── 01A-Forecast-PM2.5_ML-All.ipynb ├── 01B-Forecast-PM2.5_ML-Data.ipynb ├── 01C-Forecast-PM2.5_ML-Data-Paper.ipynb ├── 01D-Forecast-PM2.5_ML-TaylorDiagram.ipynb ├── 02A-Forecast-PM2.5_Stat-All.ipynb ├── 02B-Forecast-PM2.5_Stat-Data.ipynb ├── 02C-Forecast-PM2.5_Stat-Data-Paper.ipynb ├── 02D-Forecast-PM2.5_Stat-TaylorDiagram.ipynb ├── 03A-Forecast-PM2.5_Transformer-All.ipynb ├── 03A-Forecast-PM2.5_Transformer-All.py ├── 03B-Forecast-PM2.5_Transformer-Data.ipynb ├── 03C-Forecast-PM2.5_Transformer-Data-Paper.ipynb ├── 03D-Forecast-PM2.5_Transformer-Data-LinePlot.ipynb ├── 04A-Forecast-PM2.5_DL-All.ipynb ├── 04B-Forecast-PM2.5_DL-Data.ipynb ├── 04C-Forecast-PM2.5_DL-Data-Paper.ipynb ├── 04D-Forecast-PM2.5_DL-TaylorDiagram.ipynb ├── A0-Transformer-Basic.ipynb ├── A1-Transformer Time-Series.ipynb ├── A2-Time2Vec.ipynb ├── DM001-AirQualityUCI.ipynb ├── DM002-Beijing.ipynb ├── DS001-Delhi Dataset Creation.ipynb ├── DS002-New York Dataset Creation.ipynb ├── DS003-Seoul Dataset Creation.ipynb ├── DS004-Ulaanbaatar Dataset Creation.ipynb ├── DS005-Skopje Dataset Creation.ipynb ├── datasets │ ├── air_quality_uci_dataset.pkl │ ├── beijing_pm10_dataset.pkl │ ├── beijing_pm25_dataset.pkl │ ├── delhi_dataset.pkl │ ├── nyc_dataset.pkl │ ├── seoul_dataset.pkl │ ├── skopje_dataset.pkl │ └── ulaanbaatar_dataset.pkl ├── README.txt ├── mlenv-packages.txt ├── taylor_diagram.py └── tstransformer.py Note --- 1. For reproducing results, follow the notebook sequence from 00A - 04D. 2. DS notebook series are to understand how datasets were created. 3. DM series show data manipulation from original data. 4. A series are practice environments for Transformer experimentation. 5. tstransformer.py is the python script housing the PolTrans architecture. 6. taylor_diagram.py contains custom code for the Taylor Diagrams supplied in the figures.
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