Ensemble of Hierarchical Temporal Memory for Anomaly Detection

Abstract

Hierarchical Temporal Memory (HTM) is a continuously learning algorithm derived from neuroscience that models spatial and temporal streaming data. It was demonstrated that HTM produces a good performance in predicting unusual patterns or anomaly detection in univariate datasets. In this paper, we deploy the HTM algorithm for the anomaly detection problem in multivariate datasets, which are more common in practical scenarios. We first investigate the implementation of HTM using multi-encoders for multiple variables and analyze its performance in different parameter settings. Then, we introduce a new framework for ensemble learning by using single-encoder HTMs as weak learners. We carried out experiments on public datasets in different dimensions. Our experimental results show that our new approach outperforms the multi-encoder implementation of the HTM algorithm.