By Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han

Outlier (or anomaly) detection is a really large box which has been studied within the context of a big variety of examine components like records, information mining, sensor networks, environmental technology, dispensed platforms, spatio-temporal mining, and so forth. preliminary study in outlier detection considering time series-based outliers (in statistics). in view that then, outlier detection has been studied on a wide number of information types Read more...


in comparison to common outlier detection, recommendations for temporal outlier detection are very diverse. This booklet offers an organised photograph of either fresh and previous study in temporal outlier Read more...

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On the other hand, HMMs are interpretable and theoretically well motivated. , 2003]. Unsupervised OLAP-Based Approach Besides traditional uni-variate time series data, richer time series are quite popular. For example, a time series database may contain a set of time series, each of which are associated with multidimensional attributes. us, the database can be represented using an OLAP cube, where the time series could be associated with each cell as a measure. It is desirable that this search can automatically find anomalies like: the sales of sports apparel in the past year to one subgroup, males in the same age group who are married and have at least one child, have been declining even though overall group sales have been increasing.

3 CONCLUSIONS AND SUMMARY A significant amount of work has been performed in the area of time series outliers both in the statistics community and in the data mining community. We discussed two main types of outlier detection techniques for time series studied in the data mining community. e first part was about techniques to detect outliers over a database of time series, whereas the second part dealt with outliers within a single time series. In the former case, individual time series are outliers, whereas in the latter case, portions of a time series are outliers.

If there is already a state assigned to the next l -gram, then we simply create a transition to that state, and label it with the k elements as in the previous case. However, if the next l -gram doesn’t have any state assigned to it, we create a transition back to the current state, and assign the new l -gram to the current state (where it joins whatever l -grams were assigned to that state previously). 1. OUTLIERS IN TIME SERIES DATABASES 13 [Salvador and Chan, 2005] describe a different and a very interesting way of using FSAs for anomaly detection.

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