Research on time series generation and anomaly detection model based on variational autoencoder

Guo Changlin; Zhou Zaiyan; Liu Chunxiu; Li Wanbin; Liu Yimin; Li Longtan; Jin Guiyue; Xue Zhiwei

doi:10.11959/j.issn.2096-3750.2026.00499

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Research on time series generation and anomaly detection model based on variational autoencoder

Theory and Technology | 更新时间：2026-04-16

- Research on time series generation and anomaly detection model based on variational autoencoder
- Chinese Journal on Internet of Things Vol. 10, Issue 1, Pages: 237-249(2026)
- 作者机构：
  
  国网山东省电力公司德州供电公司，山东德州 253000
- 作者简介：
- 基金信息：
  
  State Grid Shandong Electric Power Company Science and Technology Project(520608240006)
- DOI：10.11959/j.issn.2096-3750.2026.00499
  CLC： TN915.08
- Received：10 June 2025，
  
  Revised：2025-03-24，
  
  Accepted：16 June 2025，
  
  Published：30 March 2026
- 稿件说明：
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郭昌林,周在彦,刘春秀等.基于变分自编码器的时间序列生成及异常检测模型研究[J].物联网学报,2026,10(01):237-249.

Guo Changlin,Zhou Zaiyan,Liu Chunxiu,et al.Research on time series generation and anomaly detection model based on variational autoencoder[J].Chinese Journal on Internet of Things,2026,10(01):237-249.
郭昌林,周在彦,刘春秀等.基于变分自编码器的时间序列生成及异常检测模型研究[J].物联网学报,2026,10(01):237-249. DOI： 10.11959/j.issn.2096-3750.2026.00499.

Guo Changlin,Zhou Zaiyan,Liu Chunxiu,et al.Research on time series generation and anomaly detection model based on variational autoencoder[J].Chinese Journal on Internet of Things,2026,10(01):237-249. DOI： 10.11959/j.issn.2096-3750.2026.00499.

摘要

异常检测在工业设备故障监测等能源物联网场景中具有重要的应用价值，能够帮助物联网系统实时识别时间序列中的异常模式，从而提升系统的安全性、稳定性和运维效率。然而，时序数据的稀缺性是制约模型性能的主要瓶颈之一，这主要源于高质量标注的时序数据获取成本高昂，以及工业生产等场景下的时序数据采集条件有限，难以覆盖各种可能的情境。传统的数据增强方法难以有效捕获异常事件的复杂性和多样性，进一步制约了检测模型的性能提升。为此，提出了一种基于时间序列生成的数据增强方法，以提高模型在数据稀缺条件下的异常检测能力。该方法利用变分自编码器生成模型，在稀缺数据条件下合成具有真实性和多样性的时序数据，从而有效缓解数据稀缺性对模型性能的限制，显著提升异常检测的鲁棒性和准确性。实验结果表明，该方法在智能制造等能源物联网场景中具有良好的适应性，为构建高效、智能的异常检测系统提供了有效的技术支持。

Abstract

Anomaly detection is proving to be a key capability in energy Internet of things(IoT) scenarios such as industrial equipment fault monitoring

enabling IoT systems to identify abnormal patterns in time-series data in real time

thereby improving system security

stability

and operational efficiency. However

the scarcity of time-series data is emerging as a major bottleneck limiting model performance

mainly due to the high cost of acquiring high-quality labeled data and the limited data collection conditions in industrial production

which make it difficult to cover all possible scenarios. Traditional data augmentation methods are increasingly being regarded as inadequate for capturing the complexity and diversity of abnormal events

further constraining the performance of detection models. To address these issues

a data augmentation method based on time-series generation was proposed to enhance the anomaly detection capability of models under data-scarce conditions. The method utilized a variational autoencoder generative model to synthesize realistic and diverse time-series data from limited samples

thereby mitigating the impact of data scarcity on model performance and significantly improving the robustness and accuracy of anomaly detection. Experimental results demonstrated that the proposed method exhibited good adaptability in energy IoT scenarios such as smart manufacturing

providing effective technical support for building efficient and intelligent anomaly detection systems.

关键词

Keywords

references

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