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onlitiaAlgorithms

Anomalies

Overlay a band on the metric, showing the expected behavior of a series based on past values.

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Has the possible values of 1, 2, or 3.

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Outliers

Highlight outliers series.

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• Tolerance = 0.8
  In the first example , eps is 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 , eps is 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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Forecast

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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• Seasonal Forecast (Prophet):
  Prophet is a sophisticated forecasting model designed to handle time series data with seasonal patterns and holiday effects. This algorithm is especially effective for data that shows recurring patterns (e.g., hourly, daily, weekly) and is capable of capturing seasonality and trends. Seasonal forecasting is suitable for applications with clear cyclical behaviors.

Arguments

The Seasonal Forecast function offers two options for seasonality, designed to capture the natural periodicity in log data:

• Hourly: Captures seasonality with an hourly recurrence. This option is ideal for log metrics that show patterns within a 24-hour cycle. For instance, if your logs reveal traffic spikes at the start of each hour due to scheduled tasks, or if error logs increase during peak hours (e.g., lunchtime or late evening), the hourly setting can help forecast these recurring events and detect deviations from the expected hourly pattern.

• Daily: Captures seasonality with a daily recurrence. This option is suitable for logs that follow a daily pattern, such as application logs that surge every morning when users start their workday or error logs that peak every evening due to heavy batch processing or data backups. By selecting the daily setting, you can anticipate daily log trends and based on these expected patterns.

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