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

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 Functi...

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Vydané v:	International journal of parallel programming Ročník 48; číslo 3; s. 534 - 548
Hlavní autori:	Ding, Zengyu, Mei, Gang, Cuomo, Salvatore, Li, Yixuan, Xu, Nengxiong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2020 Springer Nature B.V
Predmet:	Algorithms Basis functions Computer Science Estimation Gene loci Internet of Things Interpolation Processor Architectures Questionnaires Radial basis function Software Engineering/Programming and Operating Systems Special Issue on Emerging Technology for Software Defined Network Enabled Internet of Things Theory of Computation Time series Interpolation Internet of Things (IoT) Data imputation Missing values Time series
ISSN:	0885-7458, 1573-7640
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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 k NN 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.
AbstractList	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 k NN 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. 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.
Author	Li, Yixuan Xu, Nengxiong Ding, Zengyu Mei, Gang Cuomo, Salvatore
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Cites_doi	10.1016/j.biosystemseng.2017.09.007 10.1016/j.comcom.2014.09.008 10.1016/j.scs.2018.01.053 10.1098/rsos.170436 10.1016/j.future.2016.10.026 10.1016/j.compag.2017.09.033 10.1002/sam.11348 10.1109/TGRS.2013.2284489 10.1016/j.apnum.2016.10.016 10.1016/j.future.2017.08.054 10.1016/j.comnet.2016.11.007 10.1080/08839514.2018.1448143 10.1016/j.aei.2016.11.007 10.1016/j.jnca.2016.08.002 10.1016/j.jclepro.2016.10.006 10.1080/03610929208830990 10.1016/j.comnet.2018.03.012 10.1016/j.pmcj.2017.06.018 10.1080/02827589709355401 10.1016/j.knosys.2018.03.026 10.1007/s10766-017-0538-6 10.1155/2014/171574 10.1016/j.neucom.2015.03.108 10.1016/j.jnca.2017.08.017 10.1093/bioinformatics/btr597 10.1016/j.cageo.2007.07.010 10.1145/800186.810616 10.1016/j.jksuci.2016.10.003
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Snippet	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...
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SubjectTerms	Algorithms Basis functions Computer Science Estimation Gene loci Internet of Things Interpolation Processor Architectures Questionnaires Radial basis function Software Engineering/Programming and Operating Systems Special Issue on Emerging Technology for Software Defined Network Enabled Internet of Things Theory of Computation Time series
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Title	Comparison of Estimating Missing Values in IoT Time Series Data Using Different Interpolation Algorithms
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