Publication Type:

Conference Paper

Source:

2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, Coimbatore, India, India (2019)

ISBN:

9781728101675

URL:

https://ieeexplore.ieee.org/abstract/document/8821966

Keywords:

Anomaly detection, anomaly detection techniques, batch processing, Bio-inspired Machine Learning, Biological Machine intelligence, Conferences, data analysis, Data mining, Data models, data-driven world, Encoding, Hierarchical temporal memory, hierarchical temporal memory model, known point anomaly, Neurons, real-time manner, stock market dataset, stock markets, Synapses, Time series, time series data, Unsupervised learning, unsupervised manner

Abstract:

In this data-driven world, the Anomaly detection technique plays a key role in various domains. Since the amount of generated data is huge, conventional anomaly detection techniques using batch processing are inefficient since the cost of storing and processing large amounts of data incurs various costs. Therefore, the best algorithm for this task should be able to detect anomalies in a real-time manner and with no human intervention. In this paper, we discuss the application of the Hierarchical Temporal Memory algorithm to detect anomalies in real time and in an unsupervised manner. We applied this algorithm to the stock market dataset and analyzed the performance. We also applied the algorithm on an artificially created dataset with a known point anomaly. This work tests the performance of the algorithm for real-world applications and detects anomalies.

Cite this Research Publication

A. Anandharaj and Sivakumar, P. B., “Anomaly Detection in Time Series data using Hierarchical Temporal Memory Model”, in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, India, 2019.