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Information 2017, 8(2), 60; doi:10.3390/info8020060

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

1,2,3
,
1,3,* and 1,2,3,*
1
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing 210044, China
3
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Federico Tramarin
Received: 28 March 2017 / Revised: 17 May 2017 / Accepted: 19 May 2017 / Published: 24 May 2017
View Full-Text   |   Download PDF [435 KB, uploaded 24 May 2017]   |  

Abstract

In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented based on the sliding window. Then, the prediction model is established based on the history data to predict the future value. If the difference between a predicted value and a measured value is larger than the preset threshold value, the sequence point will be judged to be an outlier and then corrected. In this paper, the sliding window and parameter settings of the algorithm are discussed and the algorithm is verified on actual data. This method does not need to pre classify the abnormal points and perform fast, and can handle large scale data. The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously. View Full-Text
Keywords: time series; outlier detection; prediction model; sliding window; meteorological sensor network time series; outlier detection; prediction model; sliding window; meteorological sensor network
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Ma, L.; Gu, X.; Wang, B. Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network. Information 2017, 8, 60.

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