Introducing new scaled algorithms for improved outlier detection

The text discusses the challenges of detecting outliers in time series data, particularly when dealing with metrics that have similar values. The MAD (Median Absolute Deviation) and DBSCAN algorithms are effective in most situations, but may not be suitable when the dispersion is more meaningful in terms of overall magnitude. To address this, new scaled outlier algorithms ScaledMAD and ScaledDBSCAN have been introduced, which consider relative scales of divergence and median values to determine outliers. These algorithms can help identify anomalies without being affected by the overall scale of the metrics, making them suitable for applications where absolute deviation is more meaningful. The text also mentions that these new algorithms are part of a broader effort to improve monitoring features and provide dynamic graphing and alerting capabilities.

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