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  "Description": "The 'anomalize' package enables a \"tidy\" workflow for\ndetecting anomalies in data. The main functions are\ntime_decompose(), anomalize(), and time_recompose(). When\ncombined, it's quite simple to decompose time series, detect\nanomalies, and create bands separating the \"normal\" data from\nthe anomalous data at scale (i.e. for multiple time series).\nTime series decomposition is used to remove trend and seasonal\ncomponents via the time_decompose() function and methods\ninclude seasonal decomposition of time series by Loess (\"stl\")\nand seasonal decomposition by piecewise medians (\"twitter\").\nThe anomalize() function implements two methods for anomaly\ndetection of residuals including using an inner quartile range\n(\"iqr\") and generalized extreme studentized deviation (\"gesd\").\nThese methods are based on those used in the 'forecast' package\nand the Twitter 'AnomalyDetection' package. Refer to the\nassociated functions for specific references for these methods.",
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        "gesd",
        "iqr"
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        "decompose_twitter"
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}