Anomaly detection, also known as outlier detection, is the process of identifying data which is unusual. I have been using basic python Markov Chains or more complex python MCMC.
Anomaly detection can also be used to detect unusual time series. Bayesian networks are well suited for anomaly detection, because they can handle high dimensional data, which humans find difficult to interpret.
One typical way we can use data visualizations to identify some anomalies and these are clearly visible by plotting individual variables. More often anomalies are far more subtle, and are based on the interaction of many variables.
Here is a nice notebook on python mcmc:
I haven’t read the previous blog post on FFT. There are lots of time series analysis.
An interesting method for detection of patterns is using “Shape Search”:
But I think there are interesting things using signal processing as well for AD like Median Filter.