López-Vizcaíno, Manuel F.Nóvoa, FranciscoFernández, DiegoCacheda, Fidel2023-03-222023-03-222022M. F. López-Vizcaíno, F. J. Novoa, D. Fernández, y F. Cacheda, «Measuring Early Detection of Anomalies», IEEE Access, vol. 10, pp. 127695-127707, 2022, doi: 10.1109/ACCESS.2022.3224467.http://hdl.handle.net/2183/32744[Abstract] Early detection is a matter of growing importance in multiple domains as network security, health conditions over social network services or weather forecasts related disasters. It is not enough to make a good decision but it also needs to be made on time. In this paper, we define a method to evaluate detection of anomalies in time-aware systems. To do so, we present the early detection problem from a generic perspective, examine the evaluation metrics available and propose a new metric, named TaP (Time aware Precision). A set of experiments using three different datasets from different fields are performed in order to compare the behaviour of the different metrics. Two different approaches were followed, first a batch evaluation is performed, followed by a streaming evaluation which allows to present a more realistic behaviour of the systems. For both steps, we propose two sets of experiments. The first one using baseline models, followed by the evaluation of a set of Machine Learning algorithms results. The presented metric allows the amount of items needed to take a decision to be taken into account, not depending on the specific dataset but on the nature of the problem to solve.engAtribución 4.0 International (CC BY 4.0)http://creativecommons.org/licenses/by/3.0/es/Early detectionMachine learningNetwork securityReal-time systemsSocial network servicesTime-aware metricsjournal articleopen access10.1109/ACCESS.2022.3224467