scholarly journals Analysis of the facts of urbanization with forecasting the trend of urban population in Bangladesh: An application of ARIMA method

2021 ◽  
Vol 8 (2) ◽  
pp. 65-77
Author(s):  
Alfarunnahar Ruma
Keyword(s):  
Urban Areas ◽  
Urban Population ◽  
Time Series Data ◽  
Living Standard ◽  
Series Data ◽  
Educational Facilities ◽  
Employment Opportunities ◽  
Urban Populations ◽  
Increasing Trend ◽  
Over Time

There are some positive ramifications of urbanization, along these lines, which incorporate the employment opportunities, innovative and infrastructural progressions, enhanced transportation, and correspondence. The betterment of clinical services and educational facilities in urban areas increase the living standard. Urbanization has negative outcomes on wellbeing due essentially to contamination and packed everyday environments. The ARIMA methodology has been used to forecast urban populations (% of total) in Bangladesh up to 2030. ARIMA method considers time-series data from 1972 to 2019 to estimate the forecasting. The current study estimates an increasing trend of urban populations in Bangladesh over time. This study has contributed to creating awareness in the case of the changing urban population.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

Temporal trends of the association between extreme temperatures and hospitalisations for schizophrenia in Hefei, China from 2005 to 2014

2021 ◽  
Vol 78 (5) ◽  
pp. 364-370
Author(s):  
Rubing Pan ◽  
Qizhi Wang ◽  
Weizhuo Yi ◽  
Qiannan Wei ◽  
Jian Cheng ◽  
...  
Keyword(s):  
Time Series Data ◽  
Temporal Trends ◽  
Extreme Temperature ◽  
Series Data ◽  
Extreme Temperatures ◽  
Distributed Lag ◽  
Increasing Trend ◽  
Related Hospitalisation ◽  
Extreme Cold ◽  
And Gender

ObjectiveWe aimed to examine the temporal trends of the association between extreme temperature and schizophrenia (SCZ) hospitalisations in Hefei, China.MethodsWe collected time-series data on SCZ hospitalisations for 10 years (2005–2014), with a total of 36 607 cases registered. We used quasi-Poisson regression and distributed lag non-linear model (DLNM) to assess the association between extreme temperature (cold and heat) and SCZ hospitalisations. A time-varying DLNM was then used to explore the temporal trends of the association between extreme temperature and SCZ hospitalisations in different periods. Subgroup analyses were conducted by age (0–39 and 40+ years) and gender, respectively.ResultsWe found that extreme cold and heat significantly increased the risk of SCZ hospitalisations (cold: 1st percentile of temperature 1.19 (95% CI 1.04 to 1.37) and 2.5th percentile of temperature 1.16 (95% CI 1.03 to 1.31); heat: 97.5th percentile of temperature 1.37 (95% CI 1.13 to 1.66) and 99th percentile of temperature 1.38 (95% CI 1.13 to 1.69)). We found a slightly decreasing trend in heat-related SCZ hospitalisations and a sharp increasing trend in cold effects from 2005 to 2014. However, the risk of heat-related hospitalisation has been rising since 2008. Stratified analyses showed that age and gender had different modification effects on temporal trends.ConclusionsThe findings highlight that as temperatures rise the body’s adaptability to high temperatures may be accompanied by more threats from extreme cold. The burden of cold-related SCZ hospitalisations may increase in the future.

scholarly journals Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

2021 ◽  
Author(s):  
Sadnan Al Manir ◽  
Justin Niestroy ◽  
Maxwell Adam Levinson ◽  
Timothy Clark
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Defeasible Reasoning ◽  
Series Data ◽  
Inference Rules ◽  
Deep Networks ◽  
Evidence Graph ◽  
Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

scholarly journals The Effect of Inequality of Educational Facilities, Health, and Road Conditions toward Income Distribution in Central Java Province, Indonesia

2018 ◽  
Vol 73 ◽  
pp. 10014
Author(s):  
Antono Herry ◽  
Purnomo Adhi ◽  
Firmansyah
Keyword(s):  
Income Distribution ◽  
Gini Index ◽  
Time Series Data ◽  
Series Data ◽  
Educational Facilities ◽  
Public Facilities ◽  
Central Java ◽  
Road Condition ◽  
The Relationship ◽  
Central Java Province

This study examines the effect of inequality of public facilities, namely education, health, and road condition, on the income inequality in Central Java Province, Indonesia. By employing the time-series data of 15 years, this study analyzes the Gini index and the relationship between the Gini index and Index of public facilities by the regression model. The study finds that the inequality of the provision of public facilities affects the income distribution in Central Java, Indonesia

On the Constancy of Hedonic Wine Price Coefficients over Time

2019 ◽  
Vol 14 (2) ◽  
pp. 182-207 ◽  
Author(s):  
Benoît Faye ◽  
Eric Le Fur
Keyword(s):  
Time Series ◽  
Literature Review ◽  
Time Series Data ◽  
Hedonic Model ◽  
Series Data ◽  
Extensive Literature ◽  
Long Run ◽  
The Stability ◽  
Over Time ◽  
Jel Classifications

AbstractThis article tests the stability of the main hedonic wine price coefficients over time. We draw on an extensive literature review to identify the most frequently used methodology and define a standard hedonic model. We estimate this model on monthly subsamples of a worldwide auction database of the most commonly exchanged fine wines. This provides, for each attribute, a monthly time series of hedonic coefficients time series data from 2003 to 2014. Using a multivariate autoregressive model, we then study the stability of these coefficients over time and test the existence of structural or cyclical changes related to fluctuations in general price levels. We find that most hedonic coefficients are variable and either exhibit structural or cyclical variations over time. These findings shed doubt on the relevance of both short- and long-run hedonic estimations. (JEL Classifications: C13, C22, D44, G11)

scholarly journals Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model

2018 ◽  
Vol 5 (9) ◽  
pp. 3962
Author(s):  
Manikandan M. ◽  
Vishnu Prasad R. ◽  
Amit Kumar Mishra ◽  
Rajesh Kumar Konduru ◽  
Newtonraj A.
Keyword(s):  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Series Data ◽  
Road Traffic Accidents ◽  
Increasing Trend ◽  
Health Organization

Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.

scholarly journals PENGARUH PERTUMBUHAN EKONOMI TERHADAP KESEMPATAN KERJA DI PROVINSI SULAWESI TENGGARA

2018 ◽  
Vol 6 (2) ◽  
pp. 54
Author(s):  
Muhammad Nur Afiat
Keyword(s):  
Economic Growth ◽  
Quantitative Research ◽  
Time Series Data ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Least Square Method ◽  
Multiple Linear Regression Analysis ◽  
Least Square ◽  
Series Data ◽  
Employment Opportunities

This study was conducted with the aim to determine the effect of Economic Growth Rate on Employment Opportunities in Southeast Sulawesi Province 2000-2015. This research is a type of Quantitative research using secondary data in the form of time series data, ie from 2000-2015. Data source was obtained from Central Bureau of Statistics (BPS) and Bank Indonesia of Southeast Sulawesi Province. This study also uses multiple linear regression analysis tools with ordinary least square method (OLS) and then processed with application Eviews 8.0. The results of the study show that Economic Growth has a significant influence on Employment Opportunities in Southeast Sulawesi Province 2000-2015.

scholarly journals Indicators of the association of Unemployment in Indonesia with the Level of Employment Opportunity, GDP, and SER

2021 ◽  
Vol 1 (5) ◽  
pp. 189-206
Author(s):  
Diesta Pambayun
Keyword(s):  
Economic Growth ◽  
Gross Domestic Product ◽  
Time Series Data ◽  
School Enrollment ◽  
Domestic Product ◽  
Series Data ◽  
Employment Opportunities ◽  
Unequal Distribution ◽  
Negative Effect

Population inequality and the unequal distribution of income are indicators of unemployment in Indonesia, while unemployment plays an important role in economic growth. The increase in Gross Domestic Product (GDP) means that the level of public welfare improves in direct proportion to the gross domestic product (GDP) which is used as a measuring tool for economic conditions. School Enrollment Rates (SER) and employment opportunities are also identified as having an effect on economic growth, so it is important to conduct research using the ECM method using time series data for 1990-2019 sourced from the Central Statistics Agency (CSA). Based on the results of data processing, it can be seen that in the short and long term employment opportunities and GDP have a positive effect on unemployment. However, in the long term GDP and SER have no significant negative effect on unemployment.

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