期刊
IEEE ACCESS
卷 9, 期 -, 页码 44390-44401出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3066686
关键词
Data selection; machine learning; optimization; time series
资金
- 3KIA Project through ELKARTEK, Basque Government
This paper proposes a method for adaptively selecting optimal data points for sensor time series on a window-by-window basis, focusing on quantifying the effect of applying data selection algorithms to time series windows. The approach is validated on synthetically generated time series and the entire UCR time series public data archive.
Transmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is to effectively select a subset of significant data points in order to reduce data volumes without sacrificing the quality of the results of the subsequent analysis. This paper proposes a method for adaptively identifying optimal data point selection algorithms for sensor time series on a window-by-window basis. Thus, this contribution focuses on quantifying the effect of the application of data selection algorithms to time series windows. The proposed approach is first used on multiple synthetically generated time series obtained by concatenating multiple sources one after the other, and then validated in the entire UCR time series public data archive.
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