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.

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

Prediction of Mine Gas Emission Based on Time Series Analysis

2012 ◽  
Vol 616-618 ◽  
pp. 450-454 ◽  
Author(s):  
Hai Dong Meng ◽  
Dong Yuan Zang ◽  
Yu Chen Song
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Method ◽  
Ar Model ◽  
Prediction Methods ◽  
Gas Emission ◽  
Gas Concentration ◽  
Series Analysis ◽  
Mine Gas

Because the variation of mine gas concentration is influenced by various factors, so it’s impossible for traditional prediction methods of mine gas emission to include all the factors. To solve the problem, the paper proposed a prediction method of mine gas emission based on AR model of time series analysis. The experiment results indicated that the method can predict mine gas emission accurately.

scholarly journals GPS and InSAR Time Series Analysis: Deformation Monitoring Application in a Hydraulic Engineering Resettlement Zone, Southwest China

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ruya Xiao ◽  
Xiufeng He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Monitoring System ◽  
Land Surface ◽  
Southwest China ◽  
Deformation Monitoring ◽  
Hydraulic Engineering ◽  
Ground Movement ◽  
Series Analysis ◽  
Gps Monitoring

Booming development of hydropower in China has resulted in increasing concerns about the related resettlement issues. Both global positioning system (GPS) and persistent scatterer interferometric synthetic aperture radar (InSAR) time series analysis are applied to measuring the magnitude and monitoring the spatial and temporal variations of land surface displacement in Hanyuan, a hydraulic engineering resettlement zone, southwest China. The results from the GPS monitoring system established in Hanyuan match well the digital inclinometer results, suggesting that the GPS monitoring system can be employed as a complement to the traditional ground movement monitoring methods. The InSAR time series witness various patterns and magnitudes of deformation in the resettlement zone. Combining the two complementary techniques will overcome the limitations of the single method.

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.

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