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Comparison of Estimating Missing Values in IoT Time Series Data Using Different Interpolation Algorithms

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Abstract

When collecting the Internet of Things data using various sensors or other devices, it may be possible to miss several kinds of values of interest. In this paper, we focus on estimating the missing values in IoT time series data using three interpolation algorithms, including (1) Radial Basis Functions, (2) Moving Least Squares (MLS), and (3) Adaptive Inverse Distance Weighted. To evaluate the performance of estimating missing values, we estimate the missing values in eight selected sets of IoT time series data, and compare with those imputed by the standard kNN estimator. Our experiments indicate that in most experiments the estimation based on the Lancaster’s MLS is the best. It is also found that the number of nearest observed values for reference and the distribution of missing values could strongly affect the accuracy of imputation.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Grant Numbers 11602235 and 41772326), the College Students Innovation and Entrepreneurship Training Program (201811415014), the Fundamental Research Funds for the Central Universities (2652017086).

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Authors and Affiliations

  1. China University of Geosciences (Beijing), 100083, Beijing, China

    Zengyu Ding, Gang Mei, Yixuan Li & Nengxiong Xu

  2. Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, Italy

    Salvatore Cuomo

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  1. Zengyu Ding
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  2. Gang Mei
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  3. Salvatore Cuomo
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  4. Yixuan Li
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  5. Nengxiong Xu
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Correspondence to Gang Mei.

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Ding, Z., Mei, G., Cuomo, S. et al. Comparison of Estimating Missing Values in IoT Time Series Data Using Different Interpolation Algorithms. Int J Parallel Prog 48, 534–548 (2020). https://doi.org/10.1007/s10766-018-0595-5

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