Anomaly detection vs Outlier detection vs Extreme event detection Are anomaly detection, outlier detection and extreme event detection same or different? 
If different, what are the differences among them? 
 A: This is a bit of a philosophical question. I did write a blogpost on a similar theme a while ago. I will reproduce here much of what I wrote:
Outlier detection is when we find unusual records in datasets, and those datasets could be multivariate or time series. So, what is anomaly detection, then?
From a language point of view, an outlier and an anomaly are the same thing, it’s just one word is derived from old English and the other is Latin and Greek. And although we are based in North America, we still have a taste for the simpler old English words. 
From an academic point of view, an outlier is a genuine data point that is just far from the norm (think: Dionne quintuplets) whereas an anomaly is a data point generated by a different process (think: multiple births resulting from fertility treatments). But even academics use the terms interchangeably, because in practice the impact of an anomaly vs. an outlier is often the same.  
But what is anomaly detection for business?
If academics use the terms interchangeably, does the commercial world also? Then we get into common business usage. Anomaly detection software typically refers to software that analyzes streams of time series data in near real time. The idea is to find events that require action. A classic application is the monitoring of IT logs. Let’s say your company has several machines and processes running continuously. If one of your machines goes offline or one of the processes fails, that’s when you want an alert to go out. In fact, if one of your machines slows right down or the process is taking much longer than usual, that’s also when you want an alert to go out. The need for alerts has always been there but machine learning models can now increase the sophistication of the alerting, so that you are not waking up your support people in the middle of the night for a false alarm and you are only showing them the most important issues.
Finally, I would say an extreme event is really the same thing as a time series anomaly. So, an example of this is when I wrote another blogpost about finding extreme weather events in 80 years of Canadian weather data.
