Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our .....

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Accuracy estimation is an important issue in time series forecasting field, for example Islamic investment namely sukuk. In order to provide the accuracy estimation of forecasting, the statistical estimator has to be unbiased and has minimum error. In this study, the statistical estimator of moving average model is applied to forecast the sukuk series data. However, the accuracy of this model is questionable and in order to improve the accuracy, this study proposed a hybrid approach using artificial neural network (ANN) algorithm for moving average model. Noted that, the parameter of moving average is selected by considering the AIC, AICc and BIC criterion values, and the selected parameter eventually to be used as input layer of ANN algorithm. In order to examine the forecasting performance, the proposed algorithm used to calculate the 10 days ahead and 15 days ahead forecasting. Based on empirical result, it has shown that, the proposed algorithm helps to reduce the error estimation .....

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Forecasting is a very effortful task owing to its features which simultaneously contain linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model has been one the most widely utilized linear model in time series forecasting. Unfortunately, the ARIMA model cannot effortlessly handle nonlinear patterns alone. Thus, Support Vector Machine (SVM) model is introduced to solve nonlinear behavior in the datasets with high variance and uncertainty. The purposes of this study are twofold. First, to propose a hybrid ARIMA models using SVM. Secondly, to test the effectiveness of the proposed hybrid model using sea surface temperature (SST) data. Our investigation is based on two well-known real datasets, i.e., SST (modis) and in-situ SST (hycom). Statistical measurement such as MAE, MAPE, MSE, and RMSE are carried out to investigate the efficacy of the proposed models as compared to the previous ARIMA and SVMs models. The empirical results reveal that the proposed m.....

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Predicting arrival guest is an essential step in estimating the Malaysia economic impact, particularly in short and long-term COVID-19 crisis. Neural Network family of models has been widely used in economic and tourism. Most of the study used single layer of fed forward propagation system, it is because of less sampling variation in Neural Network. However, apparently the multilayer provides a better predicting result but its disadvantage is to deal with high sampling variation. The motivation of this study is to enhance the ability of multilayer Neural Network in predicting the arrival guest. In this study, a hybrid model based on Bootstrapping the Neural Network proposed to predict the arrival guest of Singapore in Malaysia. The weights of variables to the first hidden layer nodes are bootstrapped. The subsequent hidden layer takes the bootstrapped weights in order to obtain the Neural Network output. The prediction obtained by hybrid model has been compared with conventional multil.....

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Accurate prediction of the stock price is necessary for efficient financial decision making and reconstruction planning, especially during the COVID-19 crisis. Meanwhile, the ARIMA model with neural network approach is a hybrid statistical model that has been used widely in finance and statistics. It could solve the non-linearity problem in ARIMA. However, based on the unreliable sampling variation, the unbalance between the linearity and nonlinearity parts in the hybrid model could lead to inaccurate prediction. This matter motivates the purpose of this study, where the authors aim to balance the linearity and nonlinearity of the hybrid model towards predicting the Islamic investment during the Malaysia Movement Control Order (MCO). In this study, Islamic investment is analyzed using statistical based model of ARIMA. In order to balance linearity and nonlinearity parts of ARIMA which is outperforming the unreliable sampling variation, the aggregating bootstrap approach is used on ARIM.....

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Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics. 2023 by the authors...

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Homestay ecotourism in Malaysia has been extensively examined in terms of its concepts, approaches, activities, and community engagement. However, a comprehensive assessment of the sustainability factors pertaining to host families remains a critical area awaiting exploration. This is paramount for ensuring the long-term viability of homestays and fostering economic benefits within rural communities. The present study seeks to establish direct subjective measurements for evaluating the interplay between local communities, tourism, and resources in safeguarding sustainable homestays. Utilizing the Delphi approach, this research conducted interviews with 51 experts who were actively involved in six homestays located on the East Coast of Peninsular Malaysia. The objective was to identify key evaluation indicators pertinent to the homestay industry. The findings underscored the pivotal roles played by community resources and tourism in the sustainability of homestays. Additionally, environ.....

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The Spearman rho nonparametric correlation coefficient is widely used to measure the strength and degree of association between two variables. However, outliers in the data can skew the results, leading to inaccurate results as the Spearman correlation coefficient is sensitive toward outliers. Thus, the robust approach is used to construct a robust model which is highly resistant to data contamination. The robustness of an estimator is measured by the breakdown point which is the smallest fraction of outliers in a sample data without affecting the estimator entirely. To overcome this problem, the aim of this study is two-fold. Firstly, researchers have proposed a robust Spearman correlation coefficient model based on the MMestimator, called the MM-Spearman correlation coefficient. Secondly, to test the performance of the proposed model, it was tested by the Monte Carlo simulation and contaminated air pollution data in Kuala Terengganu, Terengganu, Malaysia. The data have been contamina.....

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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 w.....

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Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving averageleast-squares support vector machine. The second objective is to investiga.....

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