The week's pick
Reseach Article
Outliers Detection in Sensor Time Series using Robust moving Least Squares
by
Crislanio de Souza Macedo,
Jose Everardo Bessa Maia
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 33 |
| Year of Publication: 2020 |
| Authors: Crislanio de Souza Macedo, Jose Everardo Bessa Maia |
10.5120/ijais2020451884
|
Crislanio de Souza Macedo, Jose Everardo Bessa Maia . Outliers Detection in Sensor Time Series using Robust moving Least Squares. International Journal of Applied Information Systems. 12, 33 ( September 2020), 1-5. DOI=10.5120/ijais2020451884
@article{
10.5120/ijais2020451884,
author = {
Crislanio de Souza Macedo,
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
},
numpages = {9},
url = {
https://www.ijais.org/archives/volume12/number33/1097-2020451884/
},
doi = { 10.5120/ijais2020451884 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:10:40.628189+05:30
%A Crislanio de Souza Macedo
%A Jose Everardo Bessa Maia
%T Outliers Detection in Sensor Time Series using Robust moving Least Squares
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 33
%P 1-5
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract
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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Index Terms
Computer Science
Information Sciences
Keywords