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CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series

Ke Liu1, Mengxuan Li1, Jiajun Bu1

  • 1Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CSTSINR, a new model for time series anomaly detection. It effectively identifies anomalies in complex, high-frequency data by enhancing implicit neural representations.

Keywords:
Anomaly detectionDeep learningImplicit neural representationTime series

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Time series anomaly detection is vital for identifying deviations.
  • Implicit Neural Representations (INRs) model continuous functions but struggle with complex temporal patterns.
  • Existing INR methods have limitations in representing high-frequency data.

Purpose of the Study:

  • To propose CSTSINR, a novel anomaly detection model.
  • To improve the representation of complex temporal patterns in time series.
  • To enhance anomaly detection capabilities, especially for high-frequency data.

Main Methods:

  • Integrating structured feature maps and convolutional mechanisms with INR.
  • Addressing limitations of parameter prediction and point-wise query processing.
  • Leveraging continuous function learning for improved temporal continuity.

Main Results:

  • CSTSINR demonstrates superior performance over state-of-the-art methods.
  • Outperformed existing methods across ten benchmark datasets.
  • Successfully detected anomalies in high-frequency and complex time series data.

Conclusions:

  • CSTSINR offers enhanced anomaly detection for complex time series.
  • The model overcomes limitations of traditional INR-based approaches.
  • CSTSINR represents a significant advancement in time series anomaly detection.