Can anyone recommend a method for summarizing and processing high dimensional data streams efficiently and effectively for anomaly detection?
In fact, I investigated the different methods for data stream summarization (sampling, histograms, sketches, wavelets, sliding windows) and am confused about the choice. In fact, I noticed that sampling and sliding windows are general purpose and keep the raw data, while the others are task-specific and make transformations to the data.
I am interested in the first case but it may be more consuming.
