Download PDFOpen PDF in browser

Univariate Time Series Anomaly Detection Based on Variational AutoEncoder

EasyChair Preprint no. 8445

3 pagesDate: July 10, 2022


In the field of anomaly detection, the boundaries of anomalies are always blurred, and professional knowledge is required to define them, which consumes a lot of manpower and time to mark what anomalies are. In this paper, a Variational Auto-Encoder(VAE) neural network model is used, and an unsupervised learning anomaly detection model that considers both temporal dependencies and reconstructed features. In the calculus of marking outliers, we propose a two-dimensional sliding window with a clustering algorithm to solve the traditional method of judging outliers using a single threshold. Experimental results based on Yahoo Webscope dataset show that the performance can be ameliorated by the proposed method.

Keyphrases: anomaly detection, two-dimensional sliding window, variational auto-encoder

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Leehter Yao and Youwei Chang},
  title = {Univariate Time Series Anomaly Detection Based on Variational AutoEncoder},
  howpublished = {EasyChair Preprint no. 8445},

  year = {EasyChair, 2022}}
Download PDFOpen PDF in browser