GitHub - liuzh-buaa/ST-nFBST: [IJCAI2024] A PyTorch implementation of Full Bayesian Signficance Testing for Neural Networks in Traffic Forecasting

This is a PyTorch implementation of Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting, based on LibCity/Bigscity-LibCity: LibCity: An Open Library for Urban Spatial-temporal Data Mining (github.com).

The core code of ST-nFBST is in libcity/model/traffic_speed_prediction/BDCRNNVariableDecoder.py.

References:

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
libcity		libcity
Full_Bayesian_Significance_Testing_for_Neural_Networks_in_Traffic_Forecasting.pdf		Full_Bayesian_Significance_Testing_for_Neural_Networks_in_Traffic_Forecasting.pdf
Full_Bayesian_Significance_Testing_for_Neural_Networks_in_Traffic_Forecasting_Appendix_v2.pdf		Full_Bayesian_Significance_Testing_for_Neural_Networks_in_Traffic_Forecasting_Appendix_v2.pdf
ST-nFBST_IJCAI_PPT.pdf		ST-nFBST_IJCAI_PPT.pdf
analyse_result.py		analyse_result.py
analyse_testing_result.py		analyse_testing_result.py
analyse_testing_result1.py		analyse_testing_result1.py
analyse_testing_result2.py		analyse_testing_result2.py
analyse_testing_result3.py		analyse_testing_result3.py
analyse_testing_result4.py		analyse_testing_result4.py
analyse_testing_result5.py		analyse_testing_result5.py
analyse_testing_result6.py		analyse_testing_result6.py
analyse_testing_result7.py		analyse_testing_result7.py
divide_sensors.py		divide_sensors.py
get_statistic_distri.py		get_statistic_distri.py
readme.md		readme.md
run_model.py		run_model.py
visualize_sensor.py		visualize_sensor.py

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