4.6 Article

Towards Smart Data Selection From Tithe Series Using Statistical Methods

Journal

IEEE ACCESS
Volume 9, Issue -, Pages 44390-44401

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3066686

Keywords

Data selection; machine learning; optimization; time series

Funding

  1. 3KIA Project through ELKARTEK, Basque Government

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available