Outliers detect methods for time series data

被引：3
|
作者：
Liang, T. X. [1 ]
Cao, C. X. [2 ]
机构：
[1] Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, Beijing, Peoples R China
来源：
JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY | 2018年 / 21卷 / 04期
基金：
北京市自然科学基金; 中国博士后科学基金;
关键词：
Revenue management; Forecasting; Outlier; Statistics; Compress;
D O I：
10.1080/09720529.2018.1479171
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The outliers are unavoidable anomalous points. It will affect the accuracy of time series data. A method of outlier detection and correction for time series data based on statistics is designed in this paper, which can effectively detect the position of anomalous points and make correction. The numerical experimental resultson hotel room night data show that the accuracy of the compressed dataset is improved by 3.4% to 4.4% compared with the original dataset.
引用
收藏
页码：927 / 936
页数：10
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