Abstract: Methods and apparatuses for implementing customized anomaly detection. A time-series data including a plurality of data points is obtained. Anomaly detection is performed to the time-series data with an anomaly detection model. A feedback associated with an anomaly detection result of at least one data point in the time-series data is received. The anomaly detection model is updated based at least on the feedback through reinforcement learning.