manifest.json

{
    "title": "FFT",
    "description": "Implementation of https://dl.acm.org/doi/10.5555/1789574.1789615 proudly provided by members of the HPI AKITA project.",
    "inputDimensionality": "univariate",
    "version": "0.3.0",
    "authors": "Faraz Rasheed, Peter Peng, Reda Alhajj, Jon Rokne",
    "language": "Python",
    "type": "Detector",
    "mainFile": "algorithm.py",
    "learningType": "unsupervised",
    "executionStep": {
    	"parameters": [
            {
                "name": "fft_parameters",
                "type": "int",
                "defaultValue": 5,
                "optional": "true",
                "description": "Number of parameters to be used in IFFT for creating the fit."
            },{
                "name": "context_window_size",
                "type": "int",
                "defaultValue": 21,
                "optional": "true",
                "description": "Centered window of neighbors to consider for the calculation of local outliers' z_scores"
            },{
                "name": "local_outlier_threshold",
                "type": "float",
                "defaultValue": 0.6,
                "optional": "true",
                "description": "Outlier threshold in multiples of sigma for local outliers"
            },{
                "name": "max_anomaly_window_size",
                "type": "int",
                "defaultValue": 50,
                "optional": "true",
                "description": "Maximum size of outlier regions."
            },{
                "name": "max_sign_change_distance",
                "type": "int",
                "defaultValue": 10,
                "optional": "true",
                "description": "Maximum gap between two closed oppositely signed local outliers to detect a sign change for outlier region grouping."
            },
            {
              "name": "random_state",
              "type": "int",
              "defaultValue": 42,
              "optional": "true",
              "description": "Seed for the random number generator"
            }
        ],
        "modelInput": "none"
    }
}