scholarly journals Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. 63-68
Author(s):  
C. V. Obi ◽  
C. N. Okoli
Keyword(s):  
Diabetes Mellitus ◽  
Arima Model ◽  
Secondary Data ◽  
Information Criterion ◽  
R Software ◽  
Comparative Performance ◽  
Medical Practitioners ◽  
Anambra State ◽  
Related Risk ◽  
Best Fit

This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.

Use of Artificial Neural Networks and Arima to Forecasting Consumption Sawnwood of Pinus sp. in Brazil

2019 ◽  
Vol 21 (1) ◽  
pp. 51-61 ◽  
Author(s):  
D.A. Buratto ◽  
R. Timofeiczyk Junior ◽  
J.C.G.L. Silva ◽  
J.R. Frega ◽  
M.S.S.A. Wiecheteck ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Arima Model ◽  
Secondary Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Percentage Error ◽  
Explanatory Variables ◽  
Artificial Neural

The objective of this study was to analyze the application of an artificial neural networks model and an ARIMA model to predict the consumption of sawnwood of pine. For this, we use real and secondary data collected and obtained from a historical data source, corresponding to the period from 1997 to 2016, which were later tested to generate the forecast models. Based on economic and statistical criteria, six explanatory variables were used to fit the best model. The choice of the model was made based on Mean Squared Error, Mean Absolute Error, Theil U metric, Percentage Error of Forecast and Akaike value information criterion. The results indicated that the models generated through the ARIMA model presented better performance when compared to the artificial neural network. The best adjusted model estimated a reduction of 1.33% in consumption of sawnwood of pine in Brazil for the period between 2017 and 2020.

scholarly journals Forecast and Analyze the Revenue of Biman Bangladesh Airlines Limited Based on ARIMA Model

2020 ◽  
pp. 12-20
Author(s):  
Sumaiya Rahman ◽  
Shohel Ahmed ◽  
Tahrima Faruq
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Autoregressive Integrated Moving Average ◽  
Expenditure Data ◽  
Modeling And Forecasting ◽  
Limited Company ◽  
R Programming ◽  
Best Fit

Background: As a public limited company in Bangladesh, Biman Bangladesh Airlines Limited has been struggling to establish itself as a profitable company after taking many initiatives. The work presented in this article constitutes a contribution to modeling and forecasting the financial positionof Biman Bangladesh by using a time series approach.Methodology: The article demonstrates how the income and expenditure data could be utilized to forecast future profit scenarios by developing several Autoregressive Integrated Moving Average (ARIMA) time series with the regression model. Utilizing the Akaike Information Criterion (AIC) values, we identify the best fit ARIMA model and use this to forecast the financial scenarios for the subsequent years. To successfully build the model we use R Programming. Results and Conclusion: The model predicts future values of income, expenditure and using these two, the profit or loss scenarios can be used for forecasting from year 2018 to 2025. The results forecast that Income would increase or decrease in contest of the Expenditure. As a result Biman Bangladesh may have face significant losses in the years 2020, 2021 and 2024.

ANALISA PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana
Keyword(s):  
Capital Market ◽  
Stock Price ◽  
Time Series Data ◽  
Arima Model ◽  
Secondary Data ◽  
Actual Data ◽  
Series Data ◽  
Analysis Technique ◽  
The Right ◽  
The Capital Market

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.

Gestational diabetes mellitus and macrosomia: An analysis of secondary data

2018 ◽  
Vol 1 (2) ◽  
pp. 94-98
Author(s):  
Mufdlilah Mufdlilah ◽  
Fijri Rachmawati
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Maternal Morbidity ◽  
Secondary Data ◽  
Case Control ◽  
Health Issues ◽  
Control Approach ◽  
The Past ◽  
Maternal Morbidity And Mortality

Excessive fetal weight is considered as a health issues because it increases perinatal and maternal morbidity and mortality. The prevalence of macrosomia worldwide has increased over the past 2 to 3 decades. This study aimed to determine correlation between the incidence of gestational diabetes mellitus and macrosomia among pregnant women. The design of this study was analytical with a case control approach. Data used to analyze the incidence of macrosomia based on secondary data from 2014-2018. This research suggested that diabetes mellitus in pregnancy has a significant relationship with the incidence of macrosomia.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

scholarly journals Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes

2021 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Xingchen Yan ◽  
Xiaofei Ye ◽  
Jun Chen ◽  
Tao Wang ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Information Criterion ◽  
Lateral Position ◽  
Extreme Value ◽  
Optimal Number ◽  
Generalized Extreme Value ◽  
Vehicle Type ◽  
Lane Width ◽  
Bike Sharing ◽  
Mode Of Transport ◽  
Best Fit

Cycling is an increasingly popular mode of transport as part of the response to air pollution, urban congestion, and public health issues. The emergence of bike sharing programs and electric bicycles have also brought about notable changes in cycling characteristics, especially cycling speed. In order to provide a better basis for bicycle-related traffic simulations and theoretical derivations, the study aimed to seek the best distribution for bicycle riding speed considering cyclist characteristics, vehicle type, and track attributes. K-means clustering was performed on speed subcategories while selecting the optimal number of clustering using L method. Then, 15 common models were fitted to the grouped speed data and Kolmogorov–Smirnov test, Akaike information criterion, and Bayesian information criterion were applied to determine the best-fit distribution. The following results were acquired: (1) bicycle speed sub-clusters generated by the combinations of bicycle type, bicycle lateral position, gender, age, and lane width were grouped into three clusters; (2) Among the common distribution, generalized extreme value, gamma and lognormal were the top three models to fit the three clusters of speed dataset; and (3) integrating stability and overall performance, the generalized extreme value was the best-fit distribution of bicycle speed.

Glycemic Control Comparative of Metformin and Glimepiride in Monotherapy of Type 2 Diabetes Mellitus Patient at Islamic Jemursari Hospital Surabaya in 2018

2021 ◽  
Vol 71 (1) ◽  
pp. 24-29
Author(s):  
Rachma Putri Nariswari ◽  
Gwenny Ichsan Prabowo ◽  
Hermina Novida ◽  
Nurina Hasanatuludhhiyah
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Glycemic Control ◽  
Adult Population ◽  
Secondary Data ◽  
Diabetes Mellitus Patient ◽  
Significant Difference ◽  
Tissue Sensitivity

Introduction: Type 2 diabetes mellitus is caused by decreased tissue sensitivity to insulin. The prevalence of diabetes in the world has almost doubled since 1980, from 4.7% to 8.5% in adult population. Early diagnosis and treatment aimed at normalizing glycemic control are very important. The objective of this study was to evaluate and compare glycemic control of metformin and glimepiride in monotherapy of type 2 diabetes mellitus patients at Islamic Jemursari Hospital Surabaya. Method: This was a retrospective observational study using secondary data (medical record), include glycemic control (RPG) before and two months after receiving therapy of outpatients’ type 2 diabetes mellitus with metformin or glimepiride therapy in 2018. 96 samples were found that fit the inclusion criteria. The data were analyzed by Mann-Whitney test. Result: Most patients were female, aged 50-69 years old, and dosage of metformin therapy 1500 mg/day or glimepiride therapy 2 mg/day. There was no significant difference (p>0.05) of glycemic control (RPG) of metformin compared to glimepiride therapies in type 2 diabetes mellitus patients at Islamic Jemursari Hospital Surabaya in 2018. Conclusion: Metformin and glimepiride were not significantly different in glycemic control (RPG). There were patients with RPG >200 mg/dl after two months of metformin or glimepiride therapy.  

scholarly journals Childhood maltreatment and inflammation among pregnant women with gestational diabetes mellitus: A pilot study

2017 ◽  
Vol 10 (3) ◽  
pp. 120-124 ◽  
Author(s):  
Margaret Bublitz ◽  
Suzanne De La Monte ◽  
Susan Martin ◽  
Lucia Larson ◽  
Ghada Bourjeily
Keyword(s):  
Diabetes Mellitus ◽  
Pilot Study ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Birth Outcomes ◽  
Childhood Maltreatment ◽  
Secondary Data ◽  
Past Research ◽  
Increased Risk

Background Women with childhood maltreatment histories are at increased risk for adverse birth outcomes. Mechanisms explaining this link are poorly understood. Past research is limited by sampling pregnant women at low risk for adverse maternal and neonatal outcomes. Methods This pilot study was a secondary data analysis of 24 women with gestational diabetes mellitus; 17% of the sample also reported a maltreatment history. Women provided a blood sample to measure inflammatory cytokines and insulin resistance, and saliva samples to measure diurnal cortisol. Birth outcomes for past and current pregnancies were recorded. Results Histories of maltreatment were associated with elevated interleukin-15 and a marginally greater incidence of preterm delivery in current and past pregnancies. Conclusions This pilot study was the first to demonstrate an association between childhood maltreatment history and inflammatory cytokine levels in pregnant women diagnosed with gestational diabetes mellitus.

scholarly journals PROSPEK PENGEMBANGAN PALA RAKYAT DI PROVINSI LAMPUNG

2019 ◽  
Vol 7 (1) ◽  
pp. 14
Author(s):  
Fitri Yuni Lestari ◽  
Raden Hanung Ismono ◽  
Fembriarti Erry Prasmatiwi
Keyword(s):  
Arima Model ◽  
Secondary Data ◽  
Simple Random Sampling ◽  
Development Prospect ◽  
Primary Data ◽  
Survey Method ◽  
The Past ◽  
Interview Technique ◽  
Financial Feasibility ◽  
Good Potential

The research aimed to analyze financial feasibility and development prospect of nutmeg farming in Lampung Province.  This research was conducted in Gisting Subdistrict of Tanggamus Regency in which data was collected in September 2017 until October 2017 by survey method.  This research used primary and secondary data.  Primary data was used to answer financial feasibility analyze purpose and obtained by interview technique using questionnaires.  Secondary data in the past seven to night years was used to answer the purpose of prospect development of nutmeg farming.  The samples size of this research was 30 nutmeg farmers using simple random sampling.  The data was analyzed by financial feasibility and time series linier with ARIMA model.  The results of the study showed that the nutmeg farming was feasible, and development prospect of nutmeg farming based on production and nutmeg seeds export had a good potential for the future. Key words: ARIMA, development, financial, nutmeg, prospect

scholarly journals The Role of IL-37 and IL-38 in Obstetrics Abnormalities

2021 ◽  
Vol 8 ◽  
Author(s):  
Mei Wang
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Inflammatory Mediator ◽  
Inflammatory Responses ◽  
Medical Practitioners ◽  
Minimal Difference ◽  
Anti Inflammatory ◽  
Umbilical Cords

There are two fairly common complications during pregnancy, i.e., gestational diabetes mellitus (GDM) and pre-eclampsia, which are independent, but are also closely linked in prevalence in pregnant women, with potential serious adverse consequences. IL-37 and IL-38, which belong to the IL-1 superfamily, participate in anti-inflammatory responses. Dysregulation of IL-37 and IL-38 has been observed in many auto-immune diseases. IL-37 is substantially reduced in the umbilical cords and placentas of GDM subjects, but IL-37 is significantly induced in the placentas of pre-eclampsia patients, suggesting there are differential regulatory roles of IL-37 in obstetrics, despite IL-37 being an anti-inflammatory mediator. Furthermore, IL-38 is substantially increased in the umbilical cords and placentas of GDM subjects, but minimal difference is observed in the placentas from pre-eclampsia patients. These data imply that IL-38 is also regulated independently within the diseased placentas. This review provides some insight for both basic scientists and medical practitioners to manage these patients effectively.

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