Double Bootstrap Control Chart for Monitoring Sukuk Volatility at Bursa Malaysia

Lola, Muhamad Safiih; Zainuddin, Nurul Hila; Ramlee, Mohd Noor Afiq; Sofyan, Hizir

doi:10.11113/jt.v79.10410

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…This process continues until a new bootstrap dataset is formed with N samples. It was reported in [49] that bootstrapping is highly effective when applied to a small sample size (N < 30). In the case of a larger sample size, bootstrapping offers the benefits of improved accuracy in statistical estimation and increased percentage of estimation effectiveness and is independent of the sample distribution.…”

Section: Bootstrap Methodsmentioning

confidence: 99%

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

et al. 2020

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There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions.

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Section: Bootstrap Methodsmentioning

confidence: 99%

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

et al. 2020

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“…Bootstrap is a reliable method given the lack of researchers adding this method in daily cases of COVID-19 forecasting. Many studies have shown that the bootstrap resampling technique provides a more accurate estimation [17,[40][41][42].…”

Section: Limitations and Future Recommendationmentioning

confidence: 99%

Developing forecasting model for future pandemic applications based on COVID-19 data 2020–2022

et al. 2023

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Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.

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Monitoring shift on non-normal multivariate processes using T2 hotelling double Bootstrap control chart

Insiyah,

Astutik,

Soehono

2023

The 10th International Basic Science International Conference (Basic) 2022

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Double Bootstrap Control Chart for Monitoring Sukuk Volatility at Bursa Malaysia

Cited by 3 publications

References 30 publications

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

Developing forecasting model for future pandemic applications based on COVID-19 data 2020–2022

Monitoring shift on non-normal multivariate processes using T2 hotelling double Bootstrap control chart

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