Time Series Data Cleaning Method Based on Optimized ELM Prediction Constraints
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Affected by external factors, errors in time series data collected by sensors are common. Using the traditionalmethod of constraining the speed change rate to clean the errors can get good performance. However, they areonly limited to the data of stable changing speed because of fixed constraint rules. Actually, data with unevenchanging speed is common in practice. To solve this problem, an online cleaning algorithm for time series databased on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changesperiodically, we use the extreme learning machine to learn the law of speed changes from past data and predictthe speed ranges that change over time to detect the data. In order to realize online data repair, a dual-windowmechanism is proposed to transform the global optimal into the local optimal, and the traditional minimumchange principle and median theorem are applied in the selection of the repair strategy. Aiming at the problemthat 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 repaircandidate set. The experimental results obtained on the dataset show that the method proposed in this paper canget a better repair effect.
Abstract
Affected by external factors, errors in time series data collected by sensors are common. Using the traditionalmethod of constraining the speed change rate to clean the errors can get good performance. However, they areonly limited to the data of stable changing speed because of fixed constraint rules. Actually, data with unevenchanging speed is common in practice. To solve this problem, an online cleaning algorithm for time series databased on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changesperiodically, we use the extreme learning machine to learn the law of speed changes from past data and predictthe speed ranges that change over time to detect the data. In order to realize online data repair, a dual-windowmechanism is proposed to transform the global optimal into the local optimal, and the traditional minimumchange principle and median theorem are applied in the selection of the repair strategy. Aiming at the problemthat 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 repaircandidate set. The experimental results obtained on the dataset show that the method proposed in this paper canget a better repair effect.
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