  In the classic machine learning framework, models are trained on historical
data and used to predict future values. It is assumed that the data
distribution does not change over time (stationarity). However, in real-world
scenarios, the data generation process changes over time and the model has to
adapt to the new incoming data. This phenomenon is known as concept drift and
leads to a decrease in the predictive model's performance. In this study, we
propose a new concept drift detection method based on autoregressive models
called ADDM. This method can be integrated into any machine learning algorithm
from deep neural networks to simple linear regression model. Our results show
that this new concept drift detection method outperforms the state-of-the-art
drift detection methods, both on synthetic data sets and real-world data sets.
Our approach is theoretically guaranteed as well as empirical and effective for
the detection of various concept drifts. In addition to the drift detector, we
proposed a new method of concept drift adaptation based on the severity of the
drift.
