Outlier detection and classification in sensor data streams for proactive decision support systems

International Conference on Information Technologies in Business and Industry 2016

计算机科学;经济学;工业技术

Shcherbakov, M.V.^1 ; Brebels, A.^2 ; Shcherbakova, N.L.^1 ; Kamaev, V.A.^1 ; Gerget, O.M.^3 ; Devyatykh, D.^3

Volgograd State Technical University, 28, Lenina Ave., Volgograd

400066, Russia^1

KULeuven University, Oude Markt 13, Leuven

3000, Belgium^2

Tomsk Polytechnic University, 30, Lenina Ave., Tomsk

634050, Russia^3

关键词: Clustering methods;    Clustering model;    Constructive neural network;    External influences;    Forecasting modeling;    Observed systems;    Outlier Detection;    Quality assessment;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/803/1/012143/pdf
DOI  :  10.1088/1742-6596/803/1/012143

来源: IOP

【 摘 要 】

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems.