What are some useful robust and scalable approaches/methods towards anomaly detection of a time series data? I am mainly looking for some practical approaches carried out using Python, R, Java, etc. Albeit, I am also looking for some pointers to research papers/thesis etc. which could be helpful in carving a solution to the mentioned question.
As of now, I am currently studying Pavlidou's Thesis titled "Time series analysis of remotely-sensed TIR emission preceding strong earthquakes" as well as exploring the R packages xts, zoo, and hts.