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.
Highlights
Meteorological observation is the basis for the development of meteorology and atmospheric science
This paper presents an algorithm for outlier detection of temperature time series based on sliding window prediction in the meteorological sensor network
The authors of Reference [32] think that outlier detection based on time series forecasting is the most simple and intuitive method, but the predictive ability of this method depends on the prediction model, and it is difficult to determine a reasonable threshold
Summary
Meteorological observation is the basis for the development of meteorology and atmospheric science. The meteorological sensor network is defined as a network composed of meteorological sensor nodes, sink nodes, wireless communication facilities, and so on [10] It can monitor and collect many kinds of weather information, such as temperature, humidity, air pressure, and wind speed. According to the temperature data measured by the temperature and humidity sensor SHT15 [11] in the meteorological sensor network, some researchers in paper [12] have found the reasons for affecting the quality of the data They have put forward a corresponding correction model. This paper presents an algorithm for outlier detection of temperature time series based on sliding window prediction in the meteorological sensor network.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
