Driftsformer

Driftsformer is a family of machine learning models designed to address concept drift in data streams. Built on transformer architectures, driftsformer combines self-attention with drift-aware mechanisms to maintain predictive performance when the statistical properties of the input data change over time.

Design goals include robustness to various drift types—sudden, gradual, and recurring—and efficient online adaptation. The core idea is to apply temporal attention over recent observations while incorporating explicit drift signals, time embeddings, or gating to modulate the influence of past versus present information.

Typical implementations employ an online training loop that updates model parameters or ensemble weights in response to detected drift. Techniques may include drift-aware loss functions, adaptive weighting, ensemble selection, and memory modules that retain representative examples from past data.

Driftsformers may integrate drift detection modules or be trained to predict distributional change directly. They may use time-stamp embeddings, sliding windows, and multi-head attention to capture non-stationarity and recurring patterns.

Applications include sensor networks, financial time series, anomaly detection, and user-behavior modeling in environments prone to non-stationarity. Evaluation often considers predictive accuracy over time, time-to-drift detection, false alarm rate, and adaptation latency on synthetic and real-world drift benchmarks.

Limitations include higher computational cost, the need for careful calibration to avoid overreacting to noise, and data requirements for stable online learning. Ongoing work aims to improve efficiency, interpretability, and integration with existing online learning frameworks.

See also concept drift, transformers, online learning.