How to handle non-stationary data in online neural network based one-class classifier for anomaly detection? I have developed online neural network based one-class classifier and also enabled it for forgetting mechanism. So, now online learning with forgetting mechanism is possible. But how to handle if data is non-stationary. As it is one-class classification so, suppose we have two class data i.e. normal and outlier class then trained by only normal class data in online fashion. But training data might have different distribution compared to distribution of normal and outlier data in testing as their distribution are changing. So, how algorithm will handle this drift/shift.

One more doubt regarding non-stationary data handling: whether we will pass trained labelled data once and further labelled data for training are collected from unlabelled testing data?
Usually, non-stationary series can be dealt by differencing the time series once or twice. You can add verification to your tool - checking auto-correlation plot (or partial correlation plot).