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.
time series
andoutliers
you may be interested in the paper Chen and Liu (1993) Joint Estimation of Model Parameters and Outlier Effects in Time Series. You may find a related discussion in this and this posts. $\endgroup$