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    This study introduces a novel framework for adapting to concept drift in data streams, addressing both distribution changes and temporal dependencies. The proposed method enhances prediction accuracy by training on a temporally reconstructed space, effectively handling evolving data patterns.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Time Series Analysis

    Background:

    • Concept drift in data streams violates the identical-distribution assumption of traditional machine learning.
    • Existing methods primarily address distribution changes, often neglecting temporal dependencies inherent in time-series variables.
    • Simultaneously tackling concept drift and temporal dependency in data streams remains an underexplored area.

    Purpose of the Study:

    • To develop a novel framework for adapting to concept drift in data streams that accounts for temporal dependencies.
    • To theoretically validate the benefit of training predictors in a temporally reconstructed space during concept drift.
    • To introduce a new statistic for effective instance selection in evolving data streams.

    Main Methods:

    • Proving and validating that predictor training in a temporally reconstructed space reduces testing error during concept drift.
    • Designing the Drift Adaptation Regression (DAR) framework for predicting label variables in data streams with concept drift and temporal dependency.
    • Proposing and utilizing a new statistic, local drift degree (LDD+), as a drift adaptation technique within the DAR framework to discard outdated instances.

    Main Results:

    • Demonstrated that training on a temporally reconstructed space accelerates testing error decrease when concept drift occurs.
    • The proposed DAR framework effectively predicts label variables in data streams exhibiting both concept drift and temporal dependency.
    • The LDD+ statistic successfully identified and discarded outdated instances, ensuring the selection of relevant training data.

    Conclusions:

    • The novel DAR framework provides an effective solution for concept drift adaptation in data streams with temporal dependencies.
    • The theoretical validation and experimental results confirm the efficacy of temporal reconstruction for improving predictor performance under drift.
    • The LDD+ statistic offers a valuable tool for timely instance selection, enhancing the robustness of machine learning models in dynamic environments.