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

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Abstract

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.

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Acknowledgements

This work was supported by the Ministry of Science and Technology, Taiwan, R.O.C. (Grant Number MOST 108-2218-E-025-002-MY3) (Grant Number MOST 108-2218-E-001-001).

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Authors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Fukushima, Japan

    Neil Y. Yen

  2. National Taichung University of Science and Technology, Taichung City, Taiwan

    Jia-Wei Chang & Jia-Yi Liao

  3. National Chung Hsing University, Taichung City, Taiwan

    You-Ming Yong

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  1. Neil Y. Yen
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  2. Jia-Wei Chang
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  3. Jia-Yi Liao
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  4. You-Ming Yong
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Correspondence to Jia-Wei Chang.

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Yen, N.Y., Chang, JW., Liao, JY. et al. Analysis of interpolation algorithms for the missing values in IoT time series: a case of air quality in Taiwan. J Supercomput 76, 6475–6500 (2020). https://doi.org/10.1007/s11227-019-02991-7

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