scholarly journals A Simulation Comparison of Bootstrap Procedures in Periodically Correlated Time Series Models

2016 ◽  
Vol 12 (9) ◽  
pp. 6639-6643
Author(s):  
Lorena Margo ◽  
Eljona Milo
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Confidence Intervals ◽  
Periodic Structure ◽  
Programming Language ◽  
Dependent Data ◽  
Time Series Models ◽  
R Programming Language ◽  
R Programming

The presence of periodicity in data with periodic structure has become an important issue in parameter estimation. Several methods have been studied with intention estimating different parameters or constructing confidence intervals for the parameters. In this paper we investigate the performance of the bootstrap procedures designed for dependent data in the case of Periodically Correlated time series models. Several models with periodic structure are studied in this paper and we use R programming language to realize a simulation comparison of the performance of bootstrap procedures presented.

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

scholarly journals Microrisk Lab: an online freeware for predictive microbiology

2020 ◽  
Author(s):  
Yangtai Liu ◽  
Xiang Wang ◽  
Baolin Liu ◽  
Qingli Dong
Keyword(s):  
Parameter Estimation ◽  
Bacterial Growth ◽  
Model Simulation ◽  
Parameter Determination ◽  
Predictive Microbiology ◽  
Isothermal Conditions ◽  
R Programming Language ◽  
Behavior Simulation ◽  
R Programming ◽  
And Behavior

AbstractMicrorisk Lab was designed as an interactive modeling freeware to realize parameter estimation and model simulation in predictive microbiology. This tool was developed based on the R programming language and ‘Shinyapps.io’ server, and designed as a fully responsive interface to the internet-connected devices. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/ inactivation under static and non-isothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/ inactivation under static and non-isothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate dataset and/ or parameter setting. In this research, six case studies were reproduced in Microrisk Lab and compared in parallel to DMFit, GInaFiT, IPMP 2013/ GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling system in most cases. Microrisk Lab allowed for uniform user experience to implement microbial predictive modeling by its friendly interfaces, high-integration, and interconnectivity. It might become a useful tool for the microbial parameter determination and behavior simulation. Non-commercial users could freely access this application at https://microrisklab.shinyapps.io/english/.

scholarly journals Developing codes for validation of PM10, PM2.5, and O3 datasets using R programming language

2019 ◽  
Author(s):  
Ramin Nabizadeh ◽  
Mostafa Hadei
Keyword(s):  
Air Pollution ◽  
Programming Language ◽  
Assessment Method ◽  
Daily Maximum ◽  
Data Handling ◽  
R Programming Language ◽  
Wide Range ◽  
Us Epa ◽  
R Programming ◽  
Pm 10

Introduction: The wide range of studies on air pollution requires accurate and reliable datasets. However, due to many reasons, the measured concentra-tions may be incomplete or biased. The development of an easy-to-use and reproducible exposure assessment method is required for researchers. There-fore, in this article, we describe and present a series of codes written in R Programming Language for data handling, validating and averaging of PM10, PM2.5, and O3 datasets.   Findings: These codes can be used in any types of air pollution studies that seek for PM and ozone concentrations that are indicator of real concentra-tions. We used and combined criteria from several guidelines proposed by US EPA and APHEKOM project to obtain an acceptable methodology. Separate   .csv files for PM 10, PM 2.5 and O3 should be prepared as input file. After the file was imported to the R Programming software, first, negative and zero values of concentrations within all the dataset will be removed. Then, only monitors will be selected that have at least 75% of hourly concentrations. Then, 24-h averages and daily maximum of 8-h moving averages will be calculated for PM and ozone, respectively. For output, the codes create two different sets of data. One contains the hourly concentrations of the interest pollutant (PM10, PM2.5, or O3) in valid stations and their average at city level. Another is the   final 24-h averages of city for PM10 and PM2.5 or the final daily maximum 8-h averages of city for O3. Conclusion: These validated codes use a reliable and valid methodology, and eliminate the possibility of wrong or mistaken data handling and averaging. The use of these codes are free and without any limitation, only after the cita-tion to this article.

scholarly journals Spatial variation of physicochemical parameters in a constructed wetland for wastewater treatment: An example of the use of the R programming language

2021 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Junior Pastor Pérez-Molina ◽  
Carola Scholz ◽  
Roy Pérez-Salazar ◽  
Carolina Alfaro-Chinchilla ◽  
Ana Abarca Méndez ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Spatial Variation ◽  
Water Flow ◽  
Programming Language ◽  
Constructed Wetland ◽  
Physicochemical Parameters ◽  
Preferential Flow ◽  
Oxygen Demand ◽  
R Programming Language ◽  
R Programming

Introduction: The implementation of wastewater treatment systems such as constructed wetlands has a growing interest in the last decade due to its low cost and high effectiveness in treating industrial and residential wastewater. Objective: To evaluate the spatial variation of physicochemical parameters in a constructed wetland system of sub-superficial flow of Pennisetum alopecuroides (Pennisetum) and a Control (unplanted). The purpose is to provide an analysis of spatial dynamic of physicochemical parameters using R programming language. Methods: Each of the cells (Pennisetum and Control) had 12 piezometers, organized in three columns and four rows with a separation distance of 3,25m and 4,35m, respectively. The turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), ammoniacal nitrogen (N-NH4), organic nitrogen (N-org.) and phosphorous (P-PO4-3) were measured in water under in-flow and out-flow of both conditions Control and Pennisetum (n= 8). Additionally, the oxidation-reduction potential (ORP), dissolved oxygen (DO), conductivity, pH and water temperature, were measured (n= 167) in the piezometers. Results: No statistically significant differences between cells for TKN, N-NH4, conductivity, turbidity, BOD, and COD were found; but both Control and Pennisetum cells showed a significant reduction in these parameters (P<0,05). Overall, TKN and N-NH4 removal were from 65,8 to 84,1% and 67,5 to 90,8%, respectively; and decrease in turbidity, conductivity, BOD, and COD, were between 95,1-95,4%; 15-22,4%; 65,2-77,9% and 57,4-60,3% respectively. Both cells showed ORP increasing gradient along the water-flow direction, contrary to conductivity (p<0,05). However, OD, pH and temperature were inconsistent in the direction of the water flow in both cells. Conclusions: Pennisetum demonstrated pollutant removal efficiency, but presented results similar to the control cells, therefore, remains unclear if it is a superior option or not. Spatial variation analysis did not reflect any obstruction of flow along the CWs; but some preferential flow paths can be distinguished. An open-source repository of R was provided. 

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