  Practical machine learning applications involving time series data, such as
firewall log analysis to proactively detect anomalous behavior, are concerned
with real time analysis of streaming data. Consequently, we need to update the
ML models as the statistical characteristics of such data may shift frequently
with time. One alternative explored in the literature is to retrain models with
updated data whenever the models accuracy is observed to degrade. However,
these methods rely on near real time availability of ground truth, which is
rarely fulfilled. Further, in applications with seasonal data, temporal concept
drift is confounded by seasonal variation. In this work, we propose an approach
called Unsupervised Temporal Drift Detector or UTDD to flexibly account for
seasonal variation, efficiently detect temporal concept drift in time series
data in the absence of ground truth, and subsequently adapt our ML models to
concept drift for better generalization.
