  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.
