manifest.json

{
    "title": "mVALMOD",
    "description": "Implementation of https://doi.org/10.1007/s10618-020-00685-w summed up for every channel.",
    "inputDimensionality": "multivariate",
    "version": "0.3.0",
    "authors": "Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn Keogh",
    "language": "R",
    "type": "Detector",
    "mainFile": "algorithm.r",
    "learningType": "unsupervised",
    "executionStep": {
        "parameters": [
            {
                "name": "min_anomaly_window_size",
                "type": "Int",
                "defaultValue": 30,
                "optional": "false",
                "description": "Minimum sliding window size"
            },
            {
                "name": "max_anomaly_window_size",
                "type": "Int",
                "defaultValue": 40,
                "optional": "false",
                "description": "Maximum sliding window size"
            },
            {
                "name": "heap_size",
                "type": "Int",
                "defaultValue": 50,
                "optional": "true",
                "description": "Size of the distance profile heap buffer"
            },
            {
                "name": "exclusion_zone",
                "type": "Float",
                "defaultValue": 0.5,
                "optional": "true",
                "description": "Size of the exclusion zone as a factor of the window_size. This prevents self-matches."
            },
            {
                "name": "verbose",
                "type": "Int",
                "defaultValue": 1,
                "optional": "true",
                "description": "Controls logging verbosity."
            },
            {
                "name": "random_state",
                "type": "Int",
                "defaultValue": 42,
                "optional": "true",
                "description": "Seed for random number generation."
            }
        ],
        "modelInput": "none"
    }
}