Anomaly detection
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
- Example: Regularly scheduled web scraping that collects 9k records per week suddenly drops to 3k records
Anomaly detection for consumer data
For consumer data
- Season issue: consumption data of coat should increase in cold weather
- Holiday issue: consumption data of some gift e.g. moon cake should increase in special holiday e.g. Mid-Autumn Festival
Anomaly detection for string data
- Created time of the text message
- Time frequency of the text message
- Length of the text message
- NULL or empty value
- Minor differences of text content<ref>How to Measure Text Similarity: A Comprehensive Guide | by Ahmet Münir Kocaman | Medium</ref>
- Character encoding e.g. Fix garbled message text
More on: Outlier - Wikipedia
References
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