#Monitoring Cluster Dynamics for Change Detection in Time Series Collections

Dependencies: Pandas, NumPy, scipy, scikit-learn, matplotlib, tqdm (for querying SP500 stock data, also: alpha_vantage)

Quick Setup:

• Open the directory with PyCharm
• Run generate_inflate.py with argument @conf_data in working directory root (defaults to src/)
• Run clt.py @conf_inflate (again, adjust working directory). When using COREQ, you first need to run setup.py build in the BlockCorr folder and copy the library to the src folder

Run any script with -h to view the parameters you can define in the config file.

Major scripts are:

• generate_inflate.py generates the data according to the config
• clt.py runs cluster score computation, optionally including corrNorm and chen. Outputs are evaluation measures (coefficient of determination, precision, recall, f1 values) stored in csv files in the defined evaluationDir, score values are logged and stored at evaluationDir (if --storeScores), the plotted scores stored in plotDir (if --plotFile) and the cluster assignment of each time series for each time step is stored as csv file in evaluationDir (if --storeClusterAssign)
• chen.py runs chen in the original way, optionally plotting each hit