scholarly journals Completion Of A Full Course Of Primary Schooling Among All Children Everywhere By 2015: A Case Of Sub-Saharan Africa

2013 ◽  
Vol 6 (1) ◽  
pp. 147
Author(s):  
Robert Wamala
Keyword(s):  
Primary Education ◽  
Moving Average ◽  
Arima Model ◽  
Sub Saharan Africa ◽  
Primary Schooling ◽  
Target Date ◽  
Auto Regressive ◽  
Development Goals ◽  
Sub Saharan ◽  
Education Indicators

Achieving the United Nations Millennium Development Goals (MDGs) remains a major challenge, particularly in developing countries. Specifically, achieving the target of completing a full course of primary schooling among all children, which is goal two, is a major challenge for Sub-Saharan Africa. Though literature consensually suggests that the goal will not be achieved by the 2015 target date, no estimates are provided to support these claims. This study seeks to envisage the situation in Sub-Saharan Africa by the target date using an Auto-Regressive Integrated Moving Average (ARIMA) model. The investigation is based on data sourced from the World Bank publication of education indicators for the period 19702010. The data, comprising 41 observations, represent the total number of new entrants in the last grade of primary education, regardless of age, expressed as a percentage of the total population of the theoretical entrance age to the last grade of primary education. Overall, an upward trend of completion estimates presented in the results shows that progress has been made in this regard. The success attained for the region following the adoption of the MDGs in 2000 demonstrates that the goal can be achieved. The sub-optimal predictions of the situation obtained in the results nevertheless indicate that the achievement certainly will not be realized by the 2015 target date.

Malaria Disease Distribution in Sudan Using Time Series ARIMA Model

2015 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Mohammed Ibrahim Musa
Keyword(s):  
African Country ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Climate Factors ◽  
Training Set ◽  
The World ◽  
Auto Regressive ◽  
Sub Saharan ◽  
The Relationship

<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

scholarly journals Malaria Disease Distribution in Sudan Using Time Series ARIMA Model

2015 ◽  
Vol 4 (1) ◽  
pp. 7
Author(s):  
Mohammed Ibrahim Musa
Keyword(s):  
African Country ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Climate Factors ◽  
Training Set ◽  
The World ◽  
Auto Regressive ◽  
Sub Saharan ◽  
The Relationship

<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

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.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

Much Ado About Nothing? Exploring Effects of the Millennium Development Goals on the Welfare of Women in Sub-Saharan Africa

2014 ◽  
Vol 13 (3) ◽  
pp. 200-217 ◽  
Author(s):  
Margaret Lombe ◽  
Najwa S. Safadi ◽  
Scune Carrington ◽  
Harriet Mabikke ◽  
Yande Lombe
Keyword(s):  
Millennium Development Goals ◽  
Sub Saharan Africa ◽  
Much Ado About Nothing ◽  
Development Goals ◽  
Sub Saharan
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