One of the critical tasks of data stream analysis is anomaly detection. Various methods based on multiple assumptions have been reported in the literature. However, there is still a lack of experimental comparison of those methods, which makes it difficult to choose a specific one. In this paper, we compared unsupervised data stream abnormal point detection methods on various datasets with emphasis on their performance and runtime, as well as the presence of concept drift, seasonality, trend, and cycle as a characteristic of the dataset. Our experiments show that forecasting-based methods are the ones managing the best seasonality and trend, and lightweight models performing online gradient descent have a lower execution time. The details of our experiments are available online.