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    This study introduces a novel method for early time series classification, optimizing both prediction accuracy and earliness. The new approach uses a unique stopping rule to decide when to predict, outperforming existing methods.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Time Series Analysis

    Background:

    • Early classification of time series is crucial in applications requiring timely predictions.
    • The core challenge lies in balancing prediction accuracy with the earliness of classification.

    Purpose of the Study:

    • To develop a novel method for early time series classification.
    • To simultaneously optimize prediction accuracy and earliness using a cost-function-based stopping rule.

    Main Methods:

    • A framework combining multiple probabilistic classifiers.
    • A novel stopping rule (SR) explicitly designed to optimize a cost function balancing accuracy and earliness.

    Main Results:

    • The proposed method demonstrated superior performance compared to state-of-the-art techniques.
    • Evaluations on benchmark datasets confirmed improvements in both earliness and accuracy.

    Conclusions:

    • The developed early classification method effectively balances accuracy and earliness.
    • The novel stopping rule offers a significant advancement in time series analysis.