Anomaly detection: Difference between revisions
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* All values is event or odd | * All values is event or odd | ||
* The values are the same even the column is totally different | * The values are the same even the column is totally different | ||
* For consumer data | |||
** Season issue: consumption data of coat (大衣) and cold weather (winter 冬天) | |||
** Holiday issue: consumption data of special holiday e.g. Mid-Autumn Festival / Moon Festival | |||
== Anomaly detection of categorical data (qualitative variable) == | == Anomaly detection of categorical data (qualitative variable) == |
Revision as of 15:43, 3 October 2022
Outlier / Anomaly detection
Anomaly detection of numeric data
- Median
- Range Checks
- All values is event or odd
- The values are the same even the column is totally different
- For consumer data
- Season issue: consumption data of coat (大衣) and cold weather (winter 冬天)
- Holiday issue: consumption data of special holiday e.g. Mid-Autumn Festival / Moon Festival
Anomaly detection of categorical data (qualitative variable)
- Normal distribution e.g. The interest of audiences should be very different NOT coherent
Anomaly detection for time series data
- Trend
- Dramatically Increase or decrease of rows count for each time period
Anomaly detection for stirng data
- created time of the text message
- time frequency of the text message
- length of the text message
More on: Outlier - Wikipedia