Nowadays, time series data underlies countless research activities. Despite the wide range of techniques to capture and process all this information, issues such as analyzing large amounts of data and detecting unusual behaviors on them still pose a great challenge. In this context, this paper suggests SHESD+, a statistical technique that combines the Extreme Studentized Deviate (ESD) test and a decomposition procedure based on Loess to detect anomalies on time series data. The proposed technique employs robust metrics to identify anomalies in a more proper and accurate manner, even in the presence of trend and seasonal spikes. Simulation studies are carried out to evaluate the effectiveness of the SH-ESD+ using the published Numenta Anomaly Benchmark (NAB) collection. Computational results show that the SH-ESD+ performs consistently when compared against state-of-the-art and classic detection techniques.