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Decoupled Early Time Series Classification Using Varied-Length Feature Augmentation and Gradient Projection

Huiling Chen1, Ye Zhang1, Aosheng Tian1

  • 1College of Electronic Sciences and Technology, National University of Defense Technology, Changsha 410073, China.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
Summary

This study introduces a novel approach for early time series classification (ETSC) by decoupling classification and early exiting tasks. It enhances adaptability to varied data lengths and resolves objective conflicts for improved accuracy in time-sensitive applications.

Keywords:
early exitingearly time series classificationgradient projectionrandom length truncationvaried-length time series classification

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Early time series classification (ETSC) is vital for real-time applications, but existing methods struggle with variable data lengths and conflicting objectives.
  • Traditional deep learning models often use fixed-length data and predefined exit rules, limiting their adaptability.
  • Recent end-to-end frameworks using Recurrent Neural Networks address variable lengths but inadequately handle the classification-early exiting objective conflict.

Purpose of the Study:

  • To develop a robust method for early time series classification (ETSC) that effectively handles variable data lengths and reconciles classification and early exiting objectives.
  • To improve the accuracy and adaptability of time series classification models in time-sensitive scenarios.
  • To propose a novel framework that decouples ETSC into distinct varied-length time series classification (TSC) and early exiting tasks.

Main Methods:

  • Decoupled the ETSC task into varied-length TSC and early exiting subtasks.
  • Introduced a feature augmentation module using random length truncation to enhance adaptability to data length variations.
  • Projected gradients of classification and early exiting tasks into a unified direction to mitigate objective conflicts.

Main Results:

  • The proposed method demonstrated promising performance across 12 public datasets.
  • Successfully enhanced the adaptive capacity of classification subnets to varying data lengths.
  • Effectively addressed the inherent conflict between classification and early exiting objectives in ETSC.

Conclusions:

  • The decoupled approach offers a superior solution for early time series classification compared to existing methods.
  • The feature augmentation and gradient projection techniques are effective in handling data length variations and objective conflicts.
  • The method shows significant potential for real-world time-sensitive applications requiring accurate and timely classification.