If you're interested in more research level techniques, check out some of the work that's been done using minimum description length (MDL). It basically seeks to minimize the size of the model plus the size of the residual data (that the model doesn't predict) and uses Huffman compression as a proxy for the Kolmogorov complexity of a string:

"Discovering the intrinsic cardinality and dimensionality of time series using MDL" https://pdfs.semanticscholar.org/2049/50b3cd9cf2eef52f957df6...

A few papers that build on that one show how to incorporate anomaly detection (in the sense that the anomaly is incompressible).