Time series event detection is related to studying methods for detecting observations in a series with special meaning. These observations differ from the expected behavior of the data set. In data streaming scenarios, it is possible to observe an increase in the speed of data generation in time series. Therefore, adapting to time series changes becomes crucial. Thus, identifying events associated with these changes is essential for timely and correct decision-making. Although there are many methods to detect events, it is still possible to have difficulties detecting them correctly, particularly those associated with concept drift. In order to fill the gap in the literature, this work proposes a new method, named Forward and Backward Inertial Anomaly Detector (FBIAD), for detecting events in time series. It contributes by analyzing surrounding inertia around observations. FBIAD outperformed other methods both in accuracy and elapsed time.