Anomaly detection problems are particularly important in various real-word contest such as fraud detection, finance, intrusion detection and cyber-security. Several methods that are presented for outlier detection work well in peculiar fields but fail if they do not meet some characteristics. We develop algorithms that can be applied in a several areas and can help in solving many real problems. In this work we focus on ensemble methods for anomaly detection in static dataset to show that combining different base learners we achieve better performance than most of the base algorithms. Before analyzing the different combinations, i.e. the ensemble strategies, we present some base algorithms being the basic components of such methods.