Real-time Drift Detection on Time-series Data

TL;DR

This paper introduces UTDD, an unsupervised method for detecting temporal concept drift in streaming time series data, effectively handling seasonal variations without relying on ground truth labels, to improve model adaptation.

Contribution

The paper presents UTDD, a novel unsupervised approach that detects concept drift in time series data with seasonal patterns, enabling real-time model updates without ground truth.

Findings

UTDD effectively detects concept drift in seasonal time series.

UTDD outperforms existing methods in real-time drift detection.

The approach improves model accuracy by timely adaptation.

Abstract

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.