scholarly journals A Comparative Trend Analysis of Changes in Teacher Rate of Absenteeism in South Africa

2020 ◽  
Vol 10 (8) ◽  
pp. 189
Author(s):  
Steven Kayambazinthu Msosa
Keyword(s):  
South Africa ◽  
Student Performance ◽  
Basic Education ◽  
Moving Average ◽  
Percentage Error ◽  
Teacher Rate ◽  
Effective Strategies ◽  
Educational Advancement ◽  
Moving Average Model ◽  
Absence Rate

The aim of this research was to analyze the changes in the rate of teacher absenteeism among South African provinces as a major in-class factor contributing to student performance and effective learning. Time series analysis of exponential smoothing, moving average, and seasonal autoregressive integrated moving average model (SARIMA) were applied to model and assess the designed hypothesis as a major factor for educational advancement using different provincial data input from the Department of Basic Education in South Africa. The performances of all the models were analyzed using statistical indexes: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). The overall performance showed that the absence rate increased statistically significantly from 2011 to 2017. Thus, this opinion was held by more than half of the general populace depending on the province type. The findings of this research could assist the management of the basic education department in general, and in schools in particular, to understand the problem of absenteeism and thereby enabling the implementation of effective strategies that can be used to curb the practice.

scholarly journals Forecasting COVID-19 Cases Using Alpha-Sutte Indicator: A Comparison with Autoregressive Integrated Moving Average (ARIMA) Method

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
A. M. C. H. Attanayake ◽  
S. S. N. Perera
Keyword(s):  
South Africa ◽  
New Zealand ◽  
Transmission Rate ◽  
Moving Average ◽  
Specific Treatment ◽  
Study Data ◽  
Percentage Error ◽  
The Usa ◽  
Short Term Forecasting ◽  
Modelling Approach

COVID-19 is a pandemic which has spread to more than 200 countries. Its high transmission rate makes it difficult to control. To date, no specific treatment has been found as a cure for the disease. Therefore, prediction of COVID-19 cases provides a useful insight to mitigate the disease. This study aims to model and predict COVID-19 cases. Eight countries: Italy, New Zealand, the USA, Brazil, India, Pakistan, Spain, and South Africa which are in different phases of COVID-19 distribution as well as in different socioeconomic and geographical characteristics were selected as test cases. The Alpha-Sutte Indicator approach was utilized as the modelling strategy. The capability of the approach in modelling COVID-19 cases over the ARIMA method was tested in the study. Data consist of accumulated COVID-19 cases present in the selected countries from the first day of the presence of cases to September 26, 2020. Ten percent of the data were used to validate the modelling approach. The analysis disclosed that the Alpha-Sutte modelling approach is appropriate in modelling cumulative COVID-19 cases over ARIMA by reporting 0.11%, 0.33%, 0.08%, 0.72%, 0.12%, 0.03%, 1.28%, and 0.08% of the mean absolute percentage error (MAPE) for the USA, Brazil, Italy, India, New Zealand, Pakistan, Spain, and South Africa, respectively. Differences between forecasted and real cases of COVID-19 in the validation set were tested using the paired t -test. The differences were not statistically significant, revealing the effectiveness of the modelling approach. Thus, predictions were generated using the Alpha-Sutte approach for each country. Therefore, the Alpha-Sutte method can be recommended for short-term forecasting of cumulative COVID-19 incidences. The authorities in the health care sector and other administrators may use the predictions to control and manage the COVID-19 cases.

scholarly journals The Analysis of the 2008 US Financial Crisis: An Intervention Approach

2018 ◽  
Vol 10 (1) ◽  
pp. 59
Author(s):  
Katleho Daniel Makatjane ◽  
Edward Kagiso Molefe ◽  
Roscoe Bertrum Van Wyk
Keyword(s):  
South Africa ◽  
Exchange Rate ◽  
Financial Crises ◽  
Moving Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Intervention Model ◽  
Sarima Model ◽  
Error Metrics ◽  
The Impact

The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model. 

scholarly journals Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

2018 ◽  
Vol 10 (7) ◽  
pp. 2552 ◽  
Author(s):  
Minglu Ma ◽  
Min Su ◽  
Shuyu Li ◽  
Feng Jiang ◽  
Rongrong Li
Keyword(s):  
South Africa ◽  
Moving Average ◽  
Supply And Demand ◽  
Grey Model ◽  
Average Model ◽  
Total Energy Consumption ◽  
Coal Consumption ◽  
Autoregressive Integrated Moving Average ◽  
Non Linear ◽  
Moving Average Model

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.

scholarly journals The Analysis of the 2008 US Financial Crisis: An Intervention Approach

2018 ◽  
Vol 10 (1(J)) ◽  
pp. 59-68
Author(s):  
Katleho Daniel Makatjane ◽  
Edward Kagiso Molefe ◽  
Roscoe Bertrum Van Wyk
Keyword(s):  
South Africa ◽  
Exchange Rate ◽  
Financial Crises ◽  
Moving Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Intervention Model ◽  
Sarima Model ◽  
Error Metrics ◽  
The Impact

The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model. 

Modeling and forecasting pine sawtimber stumpage prices in the US South by various time series models

2010 ◽  
Vol 40 (8) ◽  
pp. 1506-1516 ◽  
Author(s):  
Bin Mei ◽  
Michael Clutter ◽  
Thomas Harris
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
The United States ◽  
Percentage Error ◽  
Vector Autoregressive ◽  
Market Risks ◽  
Long Run ◽  
Timber Sales ◽  
Moving Average Model

Among the three timberland return drivers (biological growth, timber price, and land price), timber price remains the most unpredictable. It affects not only periodic dividends from timber sales but also timber production strategies embedded in timberland management. Using various time series techniques, this study aimed to model and forecast real pine sawtimber stumpage prices in 12 southern timber regions in the United States. Under the discrete-time framework, the univariate autoregressive integrated moving average model was established as a benchmark, whereas other multivariate time series methods were applied in comparison. Under the continuous-time framework, both the geometric Brownian motion and the Ornstein–Uhlenbeck process were fitted. The results revealed that (i) the vector autoregressive model forecasted more accurately in the 1-year period by the mean absolute percentage error criterion, (ii) seven out of the 12 southern timber regions played dominant roles in the long-run equilibrium, and (iii) conditional variances and covariances from the bivariate generalized autoregressive conditional heteroscedasticity model well captured market risks.

scholarly journals MODELING AND FORECASTING CUMULATIVE EVI ANOMALIES USING SARIMA FOR BIOPHYSICAL MONITORING: A CASE STUDY IN THE PHILIPPINES

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 141-146
Author(s):  
A. J. L. Diccion ◽  
J. Z. Duran
Keyword(s):  
Vegetation Index ◽  
Moving Average ◽  
Terrestrial Ecosystems ◽  
The Philippines ◽  
Google Earth ◽  
Percentage Error ◽  
Upward Trend ◽  
Augmented Dickey Fuller ◽  
Moving Average Model ◽  
The Difference

Abstract. Understanding changes in vegetation cover that affect the biophysical conditions of a region can help in formulating policies to address current and future problems of terrestrial ecosystems such as deforestation and environmental degradation. This study focuses on developing a model that forecasts the cumulative Enhanced Vegetation Index (EVI) anomalies as a tool for biophysical conditions monitoring in the Philippines. Satellite data from MODIS MYD13Q1 V6, which contains vegetation index per pixel at 16-day intervals with a resolution of 250 meters, were utilized. The cumulative EVI anomalies per instant were calculated in Google Earth Engine by aggregating the difference of a specific data point in 2011–2020 to a reference EVI mean computed from 2001–2010. The Error-Trend-Seasonality model shows that the cumulative EVI anomalies graph is non-stationary with an upward trend and seasonality. The upward trend of the cumulative EVI anomalies indicates the improvement of vegetation in the Philippines. To check the stationarity of the cumulative EVI anomalies data, the Augmented Dickey-Fuller test was utilized and the model was generated using Seasonal Autoregressive Integrated Moving Average model. Based on the analysis, the best-fit model for the cumulative EVI anomalies is SARIMA (1,1,0)(1,1,1)12 with a mean absolute percentage error (MAPE) of 13.26%. Thus, the proposed model can be used as a tool for biophysical assessment by monitoring and forecasting changes in vegetation and contribute to attaining the UN Sustainable Development Goals 2 and 15 – ‘Eliminating Hunger’ and ‘Life on Land’.

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