P-086. Establishment of prediction model for hypertensive disorders of pregnancy by time series analysis

2021 ◽  
Vol 25 ◽  
pp. e57
Author(s):  
Yoshinori Moriyama ◽  
Shintaro Oyama ◽  
Hana Kumamoto-Goto ◽  
Sho Tano ◽  
Yoshihiko Tashima ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Hypertensive Disorders Of Pregnancy ◽  
Series Analysis ◽  
Hypertensive Disorders

scholarly journals Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

2015 ◽  
Vol 26 ◽  
pp. vii99 ◽  
Author(s):  
Yu Uneno ◽  
Kei Taneishi ◽  
Masashi Kanai ◽  
Akiko Tamon ◽  
Kazuya Okamoto ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Prognosis Prediction ◽  
Series Analysis ◽  
Prognosis Prediction Model ◽  
Terminal Prognosis

scholarly journals A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoping Yang ◽  
Zhongxia Zhang ◽  
Zhongqiu Zhang ◽  
Liren Sun ◽  
Cui Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Industrial Development ◽  
Measurement Model ◽  
Vector Autoregressive ◽  
Series Analysis ◽  
Term Prediction ◽  
Long Term Prediction

The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

scholarly journals The Study of Prediction Model of Gas Accidents Using Time Series Analysis

2014 ◽  
Vol 18 (1) ◽  
pp. 8-16
Author(s):  
Su-Kyung Lee ◽  
Young-Taeg Hur ◽  
Dong-Il Shin ◽  
Dong-Woo Song ◽  
Ki-Sung Kim
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Series Analysis

Research on Time Series Analysis Based Deformation Prediction Model

2011 ◽  
Vol 250-253 ◽  
pp. 2888-2891 ◽  
Author(s):  
Hao Zhang ◽  
Xi Shi ◽  
Li Fang Lai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Dynamic Deformation ◽  
Deformation Monitoring ◽  
Ar Model ◽  
Deformation Prediction ◽  
Series Analysis ◽  
Deformation Features ◽  
Dam Deformation

This paper introduces a method to apply time series analysis in dam deformation monitoring and prediction. We provide a simplified AR prediction model, which is relatively optimized in fitting constructive dynamic deformation features, analyzing deformation data and predicting deformation trend. We use this AR model in a certain dam’s deformation data processing, and prove it is an effective dynamic deformation prediction model.

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