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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Bibliographic Details
Published in:International journal of parallel programming 2020-06, Vol.48 (3), p.534-548
Main Authors: Ding, Zengyu, Mei, Gang, Cuomo, Salvatore, Li, Yixuan, Xu, Nengxiong
Format: Article
Language:English
Subjects:
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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Summary: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.
ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-018-0595-5