Outlier detection in time series has important applications in a wide variety of fields, such as patient health, weather forecasting, and cyber security. Unfortunately, outlier detection in time series data poses many challenges, making it difficult to establish an accurate and efficient detection method. In this thesis, we propose the Random Projection Outlier Ensemble (RPOE) method. The ensemble combines the results of a diverse set of components allowing it to accurately detect the different types of outliers. The components are based on the most efficient reconstruction-based method, the Random Projection (RP) method, which we enhance with a sliding window to better capture the dependencies of the time series and highlight context-dependent outliers. As a result, the RPOE method performs competitively when tested on different datasets. Moreover, the efficiency of the base detectors allows the RPOE method to have a remarkable run-time. Thus, with the RPOE method, we believe to have taken a step forward in the search for an effective outlier detection method.