2018/05/02 by Joanna Hodge, Victoria J. Hodge, Hodge, Victoria J. +2 · 1 citation
Computer Science · Engineering · Mathematics · #Anomaly Detection Techniques and Applications #Artificial Immune Systems Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1805.00811
arxiv created 2018/05/02 · openalex publication_date 2018/05/02 · arxiv updated 2018/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly. Hence, we compare the accuracy of six fast algorithms using a range of well-known time-series datasets. The analyses demonstrate that the choice of algorithm is task and data specific but that we can derive heuristics for choosing. Gradient Boosting Machines are generally best for classification but there is no single winner for outlier detection though Gradient Boosting Machines (again) and Random Forest are better. Hence, we recommend running evaluations of a number of algorithms using our heuristics.