Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework

• URL: http://arxiv.org/abs/2508.07085v1
• Date: Sat, 09 Aug 2025 19:39:33 GMT
• Title: Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework
• Authors: N Harshit, K Mounvik,
• Abstract summary: In applied machine learning, concept drift can significantly reduce model performance.<n>Our study proposes a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics.<n>Our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typical detection methods,such as statistical tests or reconstruction-based models,are generally reactive and not very sensitive to early detection. Our study proposes a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics and provide online drift detection. We create a distinct Trust Score methodology, which includes signals on (1) statistical and reconstruction-based drift metrics, more specifically, PSI, JSD, Transformer-AE error, (2) prediction uncertainty, (3) rules violations, and (4) trend of classifier error aligned with the combined metrics defined by the Trust Score. Using a time sequenced airline passenger data set with synthetic drift, our proposed model allows for a better detection of drift using as a whole and at different detection thresholds for both sensitivity and interpretability compared to baseline methods and provides a strong pipeline for drift detection in real time for applied machine learning. We evaluated performance using a time-sequenced airline passenger dataset having the gradually injected stimulus of drift in expectations,e.g. permuted ticket prices in later batches, broken into 10 time segments [1].In the data, our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature, and provided improved modeling over more error rates and logical violations. Therefore, a robust framework was developed to reliably monitor concept drift.

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