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November Edition 2020

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Outliers Detection in Sensor Time Series using Robust moving Least Squares

Crislanio de Souza Macedo, Jose Everardo Bessa Maia in Circuits and Systems

International Journal of Applied Information Systems
Year of Publication:2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Crislanio de Souza Macedo, Jose Everardo Bessa Maia
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  1. Crislanio Souza Macedo and Jose Everardo Bessa Maia. Outliers Detection in Sensor Time Series using Robust moving Least Squares. International Journal of Applied Information Systems 12(33):1-5, September 2020. URL, DOI BibTeX

    	author = "Crislanio de Souza Macedo and Jose Everardo Bessa Maia",
    	title = "Outliers Detection in Sensor Time Series using Robust moving Least Squares",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "September 2020",
    	volume = 12,
    	number = 33,
    	month = "September",
    	year = 2020,
    	issn = "2249-0868",
    	pages = "1-5",
    	url = "",
    	doi = "10.5120/ijais2020451884",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Sensors are ubiquitous elements, whether through smart phones and other personal devices, or via wireless sensor networks, body area networks or IoT in general. However, due to noise, intermittent operation or message loss, sensor time series often arrive with outliers at processing centers. In this work, the problem of detecting isolated outliers in sensor time series is addressed using Robust Moving Least Square prediction (RMLS). The performance of RMLS is compared against that of the Sequentially Discounting Autoregressive (SDAR), which is a well-established state of the art method. The results show that RMLS has performance compatible with SDAR in all tests, with the advantage that RMLS is less sensitive to outliers present in the predictors window.


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Outlier Detection, Sensor Time Series, Robust Moving Least Square, Sequentially Discounting Autoregressive, Linear Prediction