A Outlier Identification and Correction Method Based on Wavelet Transform
There are many outliers in air pollution time series data for various reasons.It has a serious impact on the data analysis and use.There are three main ways to identify anomalies but they each have definite limitations,especially when identifying and correcting the first category and the second category of outlier at the same time.In order to solve this problem,this paper presents a new way to identify anomalies based on wavelet transform and identify outlier by the use of the wavelet transform modulus maxima ,then pass the amendment of the outlier through inverse transform the wavelet transform coefficient.Evidence shows that this method can be used to identify and correct the two types of outlier simultaneously and the results are obvious.
Air Pollution Data Wavelet transform modulus maxima outlier
Binsheng Liu Ying Wang Xueping Hu
School of Economics & Management,Harbin Engineering University,Harbin 150001,China Department of Training,Heilongjiang Commercial School,Harbin 150001,China School of Humanities and Social Sciences,Harbin Engineering University,Harbin 150001,China
国际会议
哈尔滨
英文
1485-1489
2010-07-24（万方平台首次上网日期，不代表论文的发表时间）