Anomaly detection: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
|||
Line 16: | Line 16: | ||
== Anomaly detection for consumer data == | == Anomaly detection for consumer data == | ||
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 for string data == | == Anomaly detection for string data == |
Revision as of 15:44, 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
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 consumer data
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 for string data
- created time of the text message
- time frequency of the text message
- length of the text message
More on: Outlier - Wikipedia