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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.

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    $\begingroup$ 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. $\endgroup$
    – javlacalle
    Oct 29, 2014 at 8:34
  • $\begingroup$ 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. $\endgroup$
    – rahulkmishra
    Oct 29, 2014 at 8:49
  • $\begingroup$ 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. $\endgroup$
    – rahulkmishra
    Oct 29, 2014 at 8:49
  • $\begingroup$ Have a look at the R functions (mostly with C++ backends) in the robfilter package $\endgroup$
    – user603
    Oct 29, 2014 at 9:01
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    $\begingroup$ 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. $\endgroup$
    – javlacalle
    Oct 29, 2014 at 9:02

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Providing below a link to paper. The paper talks about several techniques to identify anomaly in time series data of disease.

In short anomaly detection methods in time series are of several types. You need to figure out the distribution of the data set first to identify which technique is the best appropriate for your dataset.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510767/

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    $\begingroup$ I am happy to believe that the paper contains relevant material, but this doesn't qualify as a very good answer. It would be much better to summarize the content in terms of naming and ideally explaining various techniques. Then the link would have a useful purpose in providing back-up. Occasionally answers can just say "Read this and it answers your question" but more often it's better to aim at answers that are as self-contained as possible. $\endgroup$
    – Nick Cox
    Oct 29, 2014 at 12:13

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