IoT Data Imputation with Incremental Multiple Linear Regression

Tao Peng (1, 2, 3) , Sana Sellami (1, 2, 3) , Omar Boucelma (1, 2, 3)

1 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
2 AMU - Aix Marseille Université
3 DIAMS - Data Integration, Analysis, and Management as Services

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.

Mots clés: IoT; Smart City; Missing value; Data Stream; Incremental Linear Regression

Domaines: Apprentissage [cs.LG]

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