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Anomaly detection: Difference between revisions
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→Anomaly detection for time series data
Tags: Mobile edit Mobile web edit |
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* Trend | * Trend | ||
* Dramatically Increase or decrease of rows count for each time period | * 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 | == 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>[https://medium.com/@ahmetmnirkocaman/how-to-measure-text-similarity-a-comprehensive-guide-6c6f24fc01fe How to Measure Text Similarity: A Comprehensive Guide | by Ahmet Münir Kocaman | Medium]</ref> | |||
* Character encoding e.g. [[Fix garbled message text]] | |||
More on: [https://en.wikipedia.org/wiki/Outlier#Identifying_outliers Outlier - Wikipedia] | More on: [https://en.wikipedia.org/wiki/Outlier#Identifying_outliers Outlier - Wikipedia] | ||
[[Category: | == References == | ||
[[Category:Data Science]] | <references /> | ||
[[Category: Data hygiene]] | |||
[[Category: Data Science]] | |||