Autoregressive based Drift Detection Method

TL;DR

This paper introduces ADDM, an autoregressive-based method for detecting concept drift in machine learning models, which outperforms existing methods and can be integrated into various algorithms.

Contribution

The paper presents a novel, theoretically guaranteed concept drift detection method called ADDM, adaptable to any machine learning model, with an additional approach for drift adaptation based on severity.

Findings

Outperforms state-of-the-art drift detection methods on synthetic and real data

Effective for detecting various types of concept drift

Applicable to diverse machine learning algorithms

Abstract

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.