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 | ||
* | * Dramatic increase or decrease in row count for each time period | ||
** Example: | ** Example: A regularly scheduled web scraper that normally collects 9k records per week suddenly drops to 3k records | ||
* Future data | * Future data: Data timestamps fall outside (after) the expected time range | ||
== Anomaly detection for consumer data == | == Anomaly detection for consumer data == | ||
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More on: [https://en.wikipedia.org/wiki/Outlier#Identifying_outliers Outlier - Wikipedia] | More on: [https://en.wikipedia.org/wiki/Outlier#Identifying_outliers Outlier - Wikipedia] | ||
== Further Reading == | |||
# [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 == | ||
<references /> | <references /> | ||
[[Category: Data hygiene]] | [[Category: Data hygiene]] | ||
[[Category: Data Science]] | [[Category: Data Science]] | ||