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IoT Data Imputation with Incremental Multiple Linear Regression

Abstract : In this paper, we address the problem related to missing data imputation in the IoT domain. More specifically, we propose an Incremental Space-Time-based model (ISTM) for repairing missing values in IoT real-time data streams. ISTM is based on Incremental Multiple Linear Regression, which processes data as follows: Upon data arrival, ISTM updates the model after reading again the intermediary data matrix instead of accessing all historical information. If a missing value is detected, ISTM will provide an estimation for the missing value based on nearly historical data and the observations of neighboring sensors of the default one. Experiments conducted with real traffic data show the performance of ISTM in comparison with known techniques.
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Submitted on : Wednesday, February 19, 2020 - 2:02:31 PM
Last modification on : Thursday, July 14, 2022 - 4:11:49 AM
Long-term archiving on: : Wednesday, May 20, 2020 - 4:02:20 PM


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  • HAL Id : hal-02484516, version 1



Tao Peng, Sana Sellami, Omar Boucelma. IoT Data Imputation with Incremental Multiple Linear Regression. Open Journal of Internet of Things, RonPub UG, 2019, 5 (1). ⟨hal-02484516⟩



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