Dynamical Short-Term Prediction of Rain Attenuation in W Band: A Time-Series Model With Simpler Structure and Higher Accuracy

2019 ◽  
Vol 61 (1) ◽  
pp. 77-86
Author(s):  
Bin Xue ◽  
Ningning Tong ◽  
Xin Xu ◽  
Xingyu He
Keyword(s):  
Time Series ◽  
Rain Attenuation ◽  
Time Series Model ◽  
Short Term ◽  
Term Prediction ◽  
W Band ◽  
Short Term Prediction

Biostatistical Analysis on COVID-19

2020 ◽  
pp. 1-12
Author(s):  
Bin Zhao ◽  
◽  
Xia Jiang ◽  
Jinming Cao ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Clinical Trials ◽  
Infectious Diseases ◽  
Mainland China ◽  
Time Series Model ◽  
Dynamics Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Biostatistical Analysis

Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainland China and buy more time for clinical trials.

scholarly journals Time Series Analysis of PM10 Concentration in Parit Raja Residential Area

2018 ◽  
Vol 7 (3.23) ◽  
pp. 15
Author(s):  
Ahmad Fauzi Raffee ◽  
Hazrul Abdul Hamid ◽  
Radin Maya Saphira Radin Mohamed ◽  
Muhammad Ismail Jaffar
Keyword(s):  
Time Series ◽  
Absolute Error ◽  
Residential Area ◽  
Time Series Model ◽  
Pm10 Concentration ◽  
Primary Data ◽  
Suitable Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

Parit Raja is one of the sub-urban area that rapidly grow due to its location containing industrial and education hub. Pollution from factories and the increasing number of vehicles are the main contributors of PM10.  Since PM10 can give the adverse effect to human health such as asthma, cardiovascular disease and lung problem, appropriate action mainly involve short-term prediction maybe required as a precaution. This research was conducted to predict the PM10 concentration using the best time series model in Parit Raja, Batu Pahat, Johor. Primary data was obtained using E-Sampler at three monitoring stations; Sekolah Menengah Kebangsaan (SMK) Tun Ismail, Kolej Kediaman Melewar and Sekolah Rendah Kebangsaan Pintas Raya. ARIMA time series model was used to predict the PM10 concentration and the most suitable model is identify using by Akaike Information Criterion (AIC). Prediction of PM10 concentration for for the next 48 hours at all monitoring locations was verified using three error measures which are mean absolute error (MAE), normalized absolute error (NAE) and root mean square error (RMSE). After comparing the time series model, the short term prediction model for station 1 is AR(1), station 2 is ARMA(1,1) and station 3 is ARMA(2,1)  based on the smallest AIC value and the best time series model that used for prediction at Parit Raja residential area is AR(1). Since the best model was identified for Parit Raja residential area, PM10 concentration can be predicted using AR(1) model to identify the value of PM10 concentration in the next day.  

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