Algorithms
Overlay a band on the metric, showing the expected behavior of a series based on past values.
Highlight outliers series.
Forecast future values based on past values.
Anomalies
Overlay a band on the metric, showing the expected behavior of a series based on past values.
Kloudfuse provides these possible implementations of anomaly detection:
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Implements the Rolling quantile algorithm.
sampling interval :
Sampling intervals are 1m, 2m, 3m, 5m, 10m, 15m, 30m, 1h, and 2h.
Numeric parameter
Has the possible values of 1, 2, or 3.
agile
Implements the SARIMA algorithm.
Numeric parameter
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.
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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."
Rolling Window size :
Rolling Windows are 1m, 2m, 3m, 5m, 10m, 15m, 30m, 1h, and 2h.
Band parameter:
Has the possible values of 1, 2, or 3.
Example
In the example above, the time series graph displays query throughput unique count of errors 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 increase in throughput errors during peak hours is flagged as an anomaly, allowing for quick detection and investigation.
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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.
agile
Implements the SARIMA algorithm.
Numeric parameter
Has the possible values of 1, 2, or 3.
robust
Implements the Seasonal decompose algorithm.
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Has the possible values of 1, 2, or 3.
Outlier
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Highlight outliers series.
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Tolerance = 0.8
In the first example ,epsis set to 0.8, making the detection process highly sensitive to deviations. As a result:Solid lines in the chart represent data points marked as outliers, where even small deviations from the normal pattern are detected.
Dotted lines indicate non-outliers, showing stable or expected behavior.
Tolerance = 5
In the second example ,epsis increased to 5. With this higher tolerance:Only significant deviations are identified as outliers, with most series marked as dotted lines (non-outliers).
Solid lines (outliers) appear only for major deviations, filtering out more minor deviations.
This setting is appropriate when you only want to capture large deviations and are not concerned with smaller fluctuations in the data.
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Forecasting
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Forecasting allows users to predict future values in a time series based on historical data, enabling proactive monitoring and resource planning. By forecasting trends and patterns, users can anticipate potential issues, optimize resource allocation, and make data-driven decisions. Our platform supports two forecasting algorithms tailored to different data characteristics and forecasting needs:
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