Machine Learning Algorithms Used For Anomaly Detection in Time Series Data

There are two most effective techniques of machine learning such as supervised and unsupervised learning.

Firstly, supervised learning is performed for training data points so that they can be classified into anomalous and non-anomalous data points. But, for supervised learning, there should be labelled anomalous data points.

Another approach for detecting anomaly is unsupervised learning. One can apply unsupervised learning to train CART so that prediction of next data points in the series could be made. To implement this, confidence interval or prediction error is made. Therefore, to detect anomalous data points Generalised ESD-Test is implemented to check which data points are present within or outside the confidence interval

The most common supervised learning algorithms are supervised neural networks, support vector machine learning, k-nearest neighbours, Bayesian networks and Decision trees.

In the case of k-nearest neighbours, the approximate distance between the data points is calculated and then the assignment of unlabeled data points is made according to the class of k-nearest neighbour.

On the other hand, Bayesian networks can encode the probabilistic relationships between the variables. This algorithm is mostly used with the combination of statistical techniques.

The most common unsupervised algorithms are self-organizing maps (SOM), K-means, C-means, expectation-maximization meta-algorithm (EM), adaptive resonance theory (ART), and one-class support vector machine.

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