I am new to the anomaly detection world and am dealing with a project to detect real-time anomalies for a time-series in a fraud detection schema. I read the answer by Rob Hyndman here and like the simplicity of it. However, I have to concerns. 1) I read that STL is not scalable for real-time analytics (see here for example). What is the best decomposition choice here beside fourier. 2) How can I use Rob Hyndman for a new/real-time event by using this approach? For example, let say we are time t and a new event happens (like a credit card activity). How can I detect if this is an anomaly or not given the dynamic and non-stationary nature of my time-series?
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Alex Man
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How do describe credit card activity as a time series? Is it like daily or hourly sum of transactions? – Sextus Empiricus Jun 13 '20 at 15:19
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@SextusEmpiricus that is true. Total number of transactions hourly. – Alex Man Jun 13 '20 at 15:51
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Could you add some more context on the kind of anomaly problem you want to solve? This question seems way too broad otherwise. – Alex R. Jun 16 '20 at 19:33