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Has the possible values of 1, 2, or 3.
agile
Implements the SARIMA algorithm.
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Has the possible values of 1, 2, or 3.
The Basic Anomaly Detection algorithm calculates a predicted range using the 25th and 75th quantiles and the interquartile range (IQR) within a rolling window. This range helps determine the expected "normal" behavior, while deviations outside this range are flagged as anomalies.
Parameters
Window: Defines the size of the rolling window used for quantile computation. A larger window smooths the data but may reduce sensitivity to sudden changes.
Band: Sets the sensitivity of anomaly detection. A narrower band makes the algorithm more sensitive to deviations, while a wider band captures more data as "normal."
Example
In the example above, the time series graph displays query throughput over a period. The gray band represents the expected range based on recent data, while red markers indicate anomalies—data points outside the predicted range. Here, a sudden drop in throughput during peak hours is flagged as an anomaly, allowing for quick detection and investigation.
Use Case
Basic Anomaly Detection is ideal for monitoring metrics with frequent, non-seasonal fluctuations, where rapid response to changes is essential. Use it to detect unexpected spikes or drops without needing to account for cyclic patterns or trends.
robust
Implements the Seasonal decompose algorithm.
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