An Evaluation of Classification and Outlier Detection Algorithms

Research output: Working paperPreprint


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.
Original languageEnglish
Number of pages4
Publication statusPublished - 2 May 2018


  • Anomaly Detection
  • Classification
  • Algorithms
  • outlier detection

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