Assuming there is a time series dataset of 3 columns: userid, timestamp, transaction_amount. There are millions of users. Years of data. What might be an easy and fast algo/ models to detect anomalies here?
STL seems to be able to handle 2 columns/ dimension well (for example, timestamp, transaction_amount per day), but doesn't seem to be able to handle 3 columns (userid, timestamp, transaction_amount) here together. Unless my understanding of STL is incorrect.
An unsupervised machine learning algo, Local Outlier Probabilities, seems to be a good fit here and provides good visualization. But when the 3 dimensional dataset is huge like this, the performance seems to be a real issue.
Kriegel H., Kröger P., Schubert E., Zimek A. LoOP: Local Outlier Probabilities. 18th ACM conference on Information and knowledge management, CIKM (2009)
Wonder if anyone might have any thoughts? Thank you!