Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods

2009 ◽  
Vol 4 (2) ◽  
pp. 117-122 ◽  
Author(s):  
E. Kovač-Andrić ◽  
J. Brana ◽  
V. Gvozdić
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Statistical Methods ◽  
Meteorological Factors ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Series Analysis ◽  
Ozone Concentrations

Meteorological factors affecting respiratory syncytial virus infection: A time‐series analysis

2020 ◽  
Vol 55 (3) ◽  
pp. 713-718 ◽  
Author(s):  
Hailin Zhang ◽  
Shunhang Wen ◽  
Jingwei Zheng ◽  
Xiaofang Chen ◽  
Fangfang Lv ◽  
...  
Keyword(s):  
Time Series ◽  
Respiratory Syncytial Virus ◽  
Virus Infection ◽  
Time Series Analysis ◽  
Respiratory Syncytial Virus Infection ◽  
Meteorological Factors ◽  
Factors Affecting ◽  
Series Analysis ◽  
Syncytial Virus

Change-Points Detections for Interrupted Time Series Analysis: A Literature Review

2013 ◽  
Vol 462-463 ◽  
pp. 187-192
Author(s):  
Jing Bo Chen ◽  
Jun Bao Zheng ◽  
Lei Yang ◽  
Ya Ming Wang
Keyword(s):  
Time Series ◽  
Literature Review ◽  
Time Series Analysis ◽  
Statistical Methods ◽  
Interrupted Time Series ◽  
Change Points ◽  
Detection Methods ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Common Purpose

General review of Change-Points detection methods applied in Interrupted Time Series Analysis for recent years. Articles from domains like meteorology, hydrology, stock analysis, sequences mining et al. are compared together. The literatures range from the 1980s to 2013. The methods are generally classified in Parametric, Semi-Parametric, and Nonparametric. Some non-statistical methods are also mentioned in this review. Characters of each method are briefly summarized. As all methods mentioned in this review share a common purpose that to detect change-points, most of them can be used in other domains after some proper adjustment.

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