A plethora of methodological approaches have been proferred in recent years within the topical field of time series anomaly detection. Despite many advancements in adjacent fields, the well-known local outlier factor algorithm has stood the test of time in this domain, with recent comparative studies indicating that it remains competitive with newer approaches in benchmark tests. In this paper, we enhance this algorithm by leveraging ensembling techniques and GPU (graphics processing unit) acceleration. To the best of our knowledge, our ensembling approach establishes a new state-of-the-art accuracy of 66.8% in respect of the well-known UCR Time Series Anomaly Detection benchmark, while our GPU implementation was approximately eleven times faster than our CPU (central processing unit) baseline.