# What are some useful robust and scalable approaches towards anomaly detection of a time series data?

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.

• I don't know which kind of data you have and what anomaly means in your context, but as you used the tags time series and outliers 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. – javlacalle Oct 29 '14 at 8:34
• Data is from a power domain in which half-hourly power consumption of each consumers is being provided. Here anomaly would mean something deviant from what the consumer has been consuming in general. For example, for a particular consumer, in general if the consumption on afternoon on weekends is more when compared to weekdays but if we find lesser consumption(it could be due to external factors like, out-of-station) then it could be labelled as anomaly or deviant from normal behavior. – rahulkmishra Oct 29 '14 at 8:49
• Then, there could be other scenarios like how a consumer becomes deviant from other similar consumers who had been similar in past. If the deviance is large , the particular consumers usage could become potential anomaly. – rahulkmishra Oct 29 '14 at 8:49
• Have a look at the R functions (mostly with C++ backends) in the robfilter package – user603 Oct 29 '14 at 9:01
• Then your data are panel data rather than time series, you have observations for several consumers at different time points. The references I mentioned may not be straightforward to apply to your context. – javlacalle Oct 29 '14 at 9:02