A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
Link to full paper: Y. Dang, Z. Chen, H. Li, H. Shu (2022). Applied Artificial Intelligence, 36(1), e2074129
Requirements
- Python 3.8
- xgboost==1.5.1
- ray==1.9.0
- ray[tune]==1.9.0
- torch==1.9.0
- numpy==1.20.3
- pandas==1.1.4
- matplotlib==3.4.2
- seaborn==0.11.2
Run the following command to install the required dependencies:
pip install -r requirements.txt
Follow the commands below to reproduce results in this study:
- Generate predictions on the test portion with provided pre-trained models:
python informer_result.py --use_pre_trained --use_nasa_test_range --pre_trained_file_name ../../train_models/best_informer.pth
python transformer_result.py --use_pre_trained --use_nasa_test_range --pre_trained_file_name ../../train_models/best_transformer.pth
python lstm_result.py --use_pre_trained --use_nasa_test_range --pre_trained_file_name ../../train_models/best_lstm.pth
python gru_result.py --use_pre_trained --use_nasa_test_range --pre_trained_file_name ../../train_models/best_gru.pth
- Generate predictions on the future portion with provided pre-trained models:
python informer_future.py --use_pre_trained --pre_trained_file_name ../../train_models/best_informer_future.pth
python transformer_future.py --use_pre_trained --pre_trained_file_name ../../train_models/best_transformer_future.pth
python lstm_future.py --use_pre_trained --pre_trained_file_name ../../train_models/best_lstm_future.pth
python gru_future.py --use_pre_trained --pre_trained_file_name ../../train_models/best_gru_future.pth
- Combine predictions on both test and future portions with pre-trained XGBoost models:
python xgboost_ensemble.py --test_pre_trained_file_name ../../train_models/xgboost_dl.pth --future_pre_trained_file_name ../../train_models/xgboost_future.pth