Anomaly detection: Difference between revisions

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== Anomaly detection for time series data ==
== Anomaly detection for time series data ==
* Trend
* Trend: For example, holiday and seasonal factors typically drive consistent, predictable shopping behavior, but an unexpected shift in product category suddenly occurs
* Dramatically Increase or decrease of rows count for each time period
* Dramatic increase or decrease in row count for each time period
** Example: A regularly scheduled web scraper that normally collects 9k records per week suddenly drops to 3k records
* Future data: Data timestamps fall outside (after) the expected time range


== Anomaly detection for consumer data ==
== Anomaly detection for consumer data ==
* 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
* 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 ==
== Anomaly detection for string data ==


* created time of the text message
* Created time of the text message
* time frequency of the text message
* Time frequency of the text message
* length 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:Data_hygiene]]
== Further Reading ==
[[Category:Data Science]]
 
# [https://www.amazon.com/Bad-Data-Handbook-Cleaning-Back-ebook/dp/B00A3IGAIA Amazon.com: Bad Data Handbook: Cleaning Up The Data So You Can Get Back To Work eBook : McCallum, Q. Ethan: Kindle Store] (ISBN: 9781449324964)
# [https://www.oreilly.com/library/view/bad-data-ji-shu-shou-ce/9789862768952/ Bad Data 技術手冊 [Book]] (ISBN: 9789862768952)
 
== References ==
 
<references />
 
[[Category: Data hygiene]]
[[Category: Data Science]]