scholarly journals Prediction of the Epidemiological Situation of Tuberculosis in Slovakia by 2040- Data Update

2021 ◽  
Author(s):  
Lukas Kober ◽  
Ivan Solovic ◽  
Vladimir Littva ◽  
Vladimir Siska
Keyword(s):  
Time Series ◽  
Slovak Republic ◽  
Time Series Prediction ◽  
Influence Factors ◽  
Prediction Method ◽  
Global Migration ◽  
The World ◽  
Data Update ◽  
Risk Communities ◽  
Epidemiological Situation

Background: Despite the available diagnostics and treatment, tuberculosis (TB) is a serious infectious disease currently occurring. Even some high-income countries in the world do not fully control it at this time. The reason for this situation is the lack of elimination programs to address the situation. The aim of the update of the prediction data was to create a presumption of TB development in Slovakia by 2040. Methods: We used the time series prediction method with exponential equalization. The basis for the calculation were historical data on the incidence of TB from 1960 to 2018 in Slovakia (data for the last 58 yr). This time series has a clearly declining level. In view of this trend, we have set a threshold, whether and when the incidence in the future will fall below 5.0 patients per 100,000 inhabitants. Results: In case of a favorable development, the limit of our incidence drop below 5.0 cases per 100 000 inhabitants in 2022, when the incidence will be 4.91 per 100 000 inhabitants. In 2040, the predicted incidence of TB should be 1.78 per 100 000 inhabitants. A gradual decline may also be related to a decrease in the population of the Slovak Republic. Conclusion: Slovakia belongs to those countries of the world where TB is under control. Increased surveillance of high-risk communities through community interventions and countries' readiness for global migration can help to influence factors that may aggravate the epidemiological situation of TB.

A New Time Series Prediction Method Based on LS-SVR and AGO

2013 ◽  
Vol 5 (6) ◽  
pp. 542-551
Author(s):  
Guo Yangming ◽  
Zhang Lu ◽  
Li Xiaolei ◽  
Ran Congbao ◽  
Ma Jiezhong
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Prediction Method ◽  
New Time

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network

2020 ◽  
Vol 52 (2) ◽  
pp. 1485-1500
Author(s):  
Jiaojiao Hu ◽  
Xiaofeng Wang ◽  
Ying Zhang ◽  
Depeng Zhang ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Prediction ◽  
Prediction Method

A Prediction Method for Nonlinear Correlative Time Series

2010 ◽  
Vol 40-41 ◽  
pp. 930-936 ◽  
Author(s):  
Cong Gui Yuan ◽  
Xin Zheng Zhang ◽  
Shu Qiong Xu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mutual Information ◽  
Orthogonal Polynomial ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Iterative Learning ◽  
Polynomial Basis

A nonlinear correlative time series prediction method is presented in this paper.It is based on the mutual information of time series and orthogonal polynomial basis neural network. Inputs of network are selected by mutual information, and orthogonal polynomial basis is used as active function.The network is trained by an error iterative learning algorithm.This proposed method for nonlinear time series is tested using two well known time series prediction problems:Gas furnace data time series and Mackey-Glass time series.

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