Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Time Series Data Cleaning Method Based on Optimized ELM Prediction Constraints

  • Guohui Ding ( School of Computer Science, Shenyang Aerospace University) ;
  • Yueyi Zhu ( School of Computer Science, Shenyang Aerospace University) ;
  • Chenyang Li ( School of Computer Science, Shenyang Aerospace University) ;
  • Jinwei Wang ( Beijing Aerospace Ares Equipment Installation Co. Ltd.) ;
  • Ru Wei ( School of Computer Science, Shenyang Aerospace University) ;
  • Zhaoyu Liu ( School of Computer Science, Shenyang Aerospace University)
  • Received : 2021.08.02
  • Accepted : 2022.08.07
  • Published : 2023.04.30
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Abstract

Affected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.

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