Analysis of interpolation algorithms for the missing values in IoT time series: a case of air quality in Taiwan

Missing values are common in the Internet of Things (IoT) environment for various reasons, including regular maintenance or malfunction. In time-series prediction in the IoT, missing values may have a relationship with the target labels, and their missing patterns result in informative missingness....

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Bibliographic Details
Published in:The Journal of supercomputing 2020-08, Vol.76 (8), p.6475-6500
Main Authors: Yen, Neil Y., Chang, Jia-Wei, Liao, Jia-Yi, Yong, You-Ming
Format: Article
Language:English
Subjects:
Air quality
Algorithms
Artificial neural networks
CAI
Compilers
Computer assisted instruction
Computer Science
Data mining
Internet of Things
Interpolation
Interpreters
Machine learning
Processor Architectures
Programming Languages
Regression analysis
Support vector machines
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Summary:Missing values are common in the Internet of Things (IoT) environment for various reasons, including regular maintenance or malfunction. In time-series prediction in the IoT, missing values may have a relationship with the target labels, and their missing patterns result in informative missingness. Thus, missing values can be a barrier to achieving high accuracy of prediction and analysis in data mining in the IoT. Although several methods have been proposed to estimate values that are missing, few studies have investigated the comparison of interpolation methods using conventional and deep learning models. There has thus far been relatively little research into interpolation methods in the IoT environment. To address these problems, this paper presents the use of linear regression, support vector regression, artificial neural networks, and long short-term memory to make time-series predictions for missing values. Finally, a full comparison and analysis of interpolation methods are presented. We believe that these findings can be of value to future work in IoT applications.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-019-02991-7