I'd like to develop an anomaly detection. I have historical data from sensors in the form of time series. The time series can be divided into data of a normal state and data of an abnormal state i.e. I have good data and bad data from sensors. Before I want to implement the actual anomaly detection, I want to investigate whether the good data and bad data can be separated by an explorative analysis. Thereby I want to find out if I can distinguish between good data and bad data. I hope to find out if anomalies can be detected later. Now I am looking for statistical methods or algorithmic procedures to detect and visualize this.
Can anyone help me or has ideas how I can analyse this?