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incremental adaptation

Incremental adaptation is a machine learning process in which a predictive model continuously and progressively updates its internal parameters to adjust to changing data distributions over time. Rather than retraining a model from scratch on complete historical datasets whenever data patterns shift, this approach updates the model step-by-step using newly arriving streaming data or mini-batches. By integrating emerging information while selectively retiring obsolete patterns, incremental adaptation enables learning systems operating in dynamic, non-stationary environments to maintain high accuracy, mitigate performance decay caused by concept drift, and operate with low computational and memory overhead.

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DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian

OrganizationsMicrosoftUniversity of Wisconsin Madison

Why you should read this

Proposes DDG-DA, a proactive adaptation framework that forecasts future streaming data distributions and resamples historical samples via a differentiable distribution distance to train predictive models before concept drift occurs.

In many real-world scenarios, we often deal with streaming data that is sequentially collected over time. Due to the non-stationary nature of the environment, the streaming data distribution may change in unpredictable ways, which is known as concept drift. To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data. However, there are still many cases that some underlying factors of environment evolution are predictable, making it possible to model the future concept drift trend of the streaming data, while such cases are not fully explored in previous work. In this paper, we propose a novel method DDG-DA, that can effectively forecast the evolution of data distribution and improve the performance of models. Specifically, we first train a predictor to estimate the future data distribution, then leverage it to generate training samples, and finally train models on the generated data. We conduct experiments on three real-world tasks (forecasting on stock price trend, electricity load and solar irradiance) and obtain significant improvement on multiple widely-used models.

Added

2026-09-26