Time Series Anomaly Detection for Smart Grids via Multiple Self-Supervised Tasks Learning-Reference-Cited by-同舟云学术

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Time Series Anomaly Detection for Smart Grids via Multiple Self-Supervised Tasks Learning

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Knowledge Graph (ICKG)
language:
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Author:

Zhang Zhenyu¹,Zhao Lin¹,Cai Dongyang²,Feng Shuming¹,Miao Jiawei³,Guan Yirun⁴,Tao Haicheng³,Cao Jie⁵

Affiliation:

1. Jiangsu Electric Power Information Technology Company,Nanjing,China

2. State Grid Jiangsu Electric Power Company,Nanjing,China

3. College of Information Engineering, Nanjing University of Finance & Economic,Nanjing,China

4. School of Accounting, Nanjing University of Finance & Economic,Nanjing,China

5. School of Management, Hefei University of Technology,Heifei,China

Funder

Key Program of National Natural Science Foundation of China

Jiangsu Provincial Key Research and Development Program

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10029343/10029943/10030049.pdf?arnumber=10030049

Reference41 articles.

1. Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series

2. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

3. Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models

4. Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

5. Deep Learning–Based Multimedia Analytics

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