High Performance Time Series Anomaly Detection Using Brain Inspired Cortical Coding Method

Meriç Yücel

doi:10.1109/ACCESS.2023.3239212

High Performance Time Series Anomaly Detection Using Brain Inspired Cortical Coding Method

2023-01-01

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DOI：10.1109/ACCESS.2023.3239212

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Accurate and automated anomaly detection in time series data sets has an increasingly important role in a wide range of applications. Inspired by coding in the cortical networks of the brain, here we introduce a novel approach for high performance real-time anomaly detection. Cortical coding method is adaptive and dynamic, consisting of self-organized networks. In the cortical coding network introduced herein, the morphological structuring is driven by a brain inspired feature extraction strategy that aims the minimization of the signal energy dissipation while increasing the information entropy of the system. We combine the cortical coding network with transform coding and multi resolution analysis for anomaly detection. As we demonstrate here, the new coding methodology provides high computational efficiency in addition to scalability with respect to target accuracy compared to the traditional clustering algorithms. A wide variety of data sets are used to demonstrate time series anomaly detection performance. In a preliminary work presented here, we detected 77.6% of the present anomalies correctly, using the same hyperparameters for every stage of the method. The results are compared with several clustering algorithms such as K-means and its variants mini-batch K-means, sequential K-means and finally with hierarchical agglomerative clustering. Additionally, the performance of all the clustering methods are compared by memorizing all input data set without performing any clustering. The cortical coding method has shown the best performance compared to the other methods. From the results achieved so far, it appears that there is still a significant room for improvement of the success rate by, specifically, performing hyperparameter and filter optimization according to the characteristics of data sets and using a more advanced fusion model at the output layer. Low time and space complexity, high generalization performance, suitability to real-time anomaly detection, and in-memory processing compatibility are distinct advantages of the cortical coding method in a variety of anomaly detection problems, such as predictive maintenance, cybersecurity, telemedicine, risk management, and transportation safety.

Keyword:

Encoding

Anomaly detection

Time series analysis

Neurons

Feature extraction

Time complexity

Heuristic algorithms

brain inspired cortical coding network

clustering

cybersecurity

discrete wavelet packet transform

entropy maximization

hierarchical agglomerative clustering

k-means

mini-batch k-means

sequential k-means

time series data

High Performance Time Series Anomaly Detection Using Brain Inspired Cortical Coding Method

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