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manifest.json
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{
"title": "SAND",
"description": "Implementation of SAND described in http://www.vldb.org/pvldb/vol14/p1717-boniol.pdf.",
"inputDimensionality": "univariate",
"version": "0.3.0",
"authors": "Sebastian Schmidl",
"language": "Python",
"type": "Detector",
"mainFile": "algorithm.py",
"learningType": "unsupervised",
"executionStep": {
"parameters": [
{
"name": "anomaly_window_size",
"type": "int",
"defaultValue": 75,
"optional": "true",
"description": "Size of the anomalous pattern; sliding windows for clustering and preprocessing are of size 3*anomaly_window_size."
},
{
"name": "n_clusters",
"type": "int",
"defaultValue": 6,
"optional": "true",
"description": "Number of clusters used in Kshape that are maintained iteratively as a normal model"
},
{
"name": "n_init_train",
"type": "int",
"defaultValue": 2000,
"optional": "true",
"description": "Number of points to build the initial model (may contain anomalies)"
},
{
"name": "iter_batch_size",
"type": "int",
"defaultValue": 500,
"optional": "true",
"description": "Number of points for each batch. Mostly impacts performance (not too small)."
},
{
"name": "alpha",
"type": "float",
"defaultValue": 0.5,
"optional": "true",
"description": "Weight decay / forgetting factor. Quite robust"
},
{
"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"
}
}