MSBD5002 Data Mining course project
- Numpy, Pandas, Scipy
- rrcf for robust random cut forest
- stumpy for matrix profile
- sranodec for spectral residual
We ensemble several methods for anomaly detection, including statistical methods (statistic_func.py), fourier transformation （fourier_transform.py）, matrix profile (matrix_profile.py), spectural residual (spectral_residual.py) and robust random cut forest (rrcf.py). Details of each method cound be found in each of the python file.
To run the program, use the following command
bash ./run.sh
It may take a few days due to the significant resources required to compute the matrix profile, although we use a 6-core GPU.
The ensembled results are saved in ensemble_results folder. We take the index with the largest confidence score to be the final detected anomaly location. The final result is in submission.csv