manifest.json

{
    "title": "STOMP",
    "description": "Implementation of https://doi.org/10.1109/ICDM.2016.0085.",
    "inputDimensionality": "univariate",
    "version": "0.3.0",
    "authors": "Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, Eamonn Keogh",
    "language": "R",
    "type": "Detector",
    "mainFile": "algorithm.r",
    "learningType": "unsupervised",
    "executionStep": {
        "parameters": [
            {
                "name": "anomaly_window_size",
                "type": "Int",
                "defaultValue": 30,
                "optional": "false",
                "description": "Size of the sliding window."
            },
            {
                "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": "n_jobs",
                "type": "Int",
                "defaultValue": 1,
                "optional": "true",
                "description": "The number of jobs to run in parallel. `-1` is not supported, defaults back to serial implementation."
            },
            {
                "name": "random_state",
                "type": "Int",
                "defaultValue": 42,
                "optional": "true",
                "description": "Seed for random number generation."
            },
            {
                "name": "use_column_index",
                "type": "int",
                "defaultValue": 0,
                "optional": "true",
                "description": "The column index to use as input for the univariate algorithm for multivariate datasets. The selected single channel of the multivariate time series is analyzed by the algorithms. The index is 0-based and does not include the index-column ('timestamp'). The single channel of an univariate dataset, therefore, has index 0."
            }
        ],
        "modelInput": "none"
    }
}