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    This study introduces an end-to-end deep neural network for time-series classification (TSC). The model enhances accuracy and interpretability using convolutional and recurrent networks with sparse group lasso regularization.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Time-series classification (TSC) is crucial for analyzing sequential data across various domains.
    • Existing models often struggle to balance classification accuracy with interpretability.
    • Feature engineering can be labor-intensive and domain-specific for TSC tasks.

    Purpose of the Study:

    • To propose a novel end-to-end deep neural network for TSC that excels in both accuracy and interpretability.
    • To develop a model that automates feature extraction, reducing the need for manual feature engineering.
    • To leverage sparse group lasso (SGL) regularization for enhanced model interpretability.

    Main Methods:

    • An integrated architecture combining convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) for temporal modeling.
    • A feedforward fully connected network incorporating sparse group lasso (SGL) regularization for final classification.
    • Joint, end-to-end training of all network components for seamless integration and optimization.

    Main Results:

    • The proposed model achieved superior classification accuracy compared to traditional CNN models on diverse TSC datasets.
    • SGL regularization effectively contributed to improved model interpretability, highlighting key features.
    • The end-to-end framework demonstrated generalizability across different TSC application domains.

    Conclusions:

    • The developed deep neural network offers a powerful and interpretable solution for time-series classification.
    • The integration of CNNs, RNNs, and SGL regularization provides a robust approach to TSC.
    • This model reduces reliance on manual feature engineering, making TSC more accessible and efficient.