Anomaly detection: Difference between revisions
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| Line 18: | Line 18: | ||
For consumer data | For consumer data | ||
* Season issue: consumption data of coat | * Season issue: consumption data of coat should increase in cold weather | ||
* Holiday issue: consumption data of special holiday e.g. Mid-Autumn | * 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 == | == Anomaly detection for string data == | ||
Latest revision as of 15:55, 3 October 2022
Outlier / Anomaly detection
Anomaly detection of numeric data[edit]
- 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)[edit]
- Normal distribution e.g. The interest of audiences should be very different NOT coherent
Anomaly detection for time series data[edit]
- Trend
- Dramatically Increase or decrease of rows count for each time period
Anomaly detection for consumer data[edit]
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[edit]
- created time of the text message
- time frequency of the text message
- length of the text message
More on: Outlier - Wikipedia