Step-by-Step Tutorial with Access Log data.
It detects anomaly in time series data frame. It employs an algorithm referred to as Seasonal Hybrid ESD (S-H-ESD), which can detect both global as well as local anomalies in the time series data by taking seasonality and trend into account. It’s built by a team at Twitter for their use on monitoring their traffics.
How to Access?
How to Configure?
- Date / Time Column - Select a Date or POSIXct data type column that holds date / time information.
- Aggregation Level - Select date / time level such as Month, Week, Day, etc. to aggregate the data.
- Value Column - Select either 'Number of Rows' or a numeric column for which you want to detect anomalies.
- Aggregation Function - Select an aggregate function such as 'sum', 'mean', etc. to aggregate the values.
- Direction of Anomaly (Optional) - The default is "both". Direction of anomaly. This can be
- "both" - Both positive and negative direction.
- "pos" - Only positive direction.
- "neg" - Only negative direction.
- With Expected Values (Optional) - The default is TRUE. Whether expected_values should be returned.
- Maximum Ratio of Anomaly Data (Optional) - The default is 0.1. The maximum ratio of anomaly data compared to the number of total data.
- Alpha (Sensitivity to Anomaly Data) (Optional) - The default is 0.05. The larger the value, the more anomaly data are captured.
- Report Only Last Values within (Optional) - The default is NULL. Find only last anomalies within a day or hour. This can be
- NULL - Find all anomalies.
- "day" - Find last anomalies within a day.
- "hr" - Find last anomalies within an hour.
- Threshold of Positive Anomaly (Optional) - The default is 'None'. If this is specified, only positive anomalies above the threshold are reported. This can be
- 'None' - No threshold.
- 'med_max' - Median of daily max values.
- 'p95' - 95th percentile of the daily max values.
- 'p99' - 99th percentile of the daily max values.
- Longer Time Span than a Month (Optional) - The default is FALSE. This should be TRUE if the time span is longer than a month.
- Piecewise Median Time Window (Optional) - The default is 2. The size of piecewise median time window (span of seasons). The unit is weeks.
How to Read the Result?
- Date / Time Column
- Value Column
- pos_anomaly - Returns TRUE if anomaly is detected in the positive detection for each row.
- pos_value - Anomaly values in the positive direction.
- neg_anomaly - Returns TRUE if anomaly is detected in the negative detection for each row.
- neg_value - Anomaly values in the negative direction.
- expected_value - The values that the model would have expected based on the underlying trend.