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The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model. [ABSTRACT FROM AUTHOR]

Dealing with stationarity remains an unsolved problem. Some of the time series data, especially crude palm oil (CPO) prices persist towards nonstationarity in the long-run data. This dilemma forces the researchers to conduct first-order difference. The basic idea is that to obtain the stationary data that is considered as a good strategy to overcome the nonstationary counterparts. An opportune remark as it is, this proxy may lead to overdifference. The CPO prices trend elements have not been attenuated but nearly annihilated. Therefore, this paper presents the usefulness of autoregressive fractionally integrated moving average (ARFIMA) model as the solution towards the nonstationary persistency of CPO prices in the long-run data. In this study, we employed daily historical Free-on-Board CPO prices in Malaysia. A comparison was made between the ARFIMA over the existing autoregressive-integrated moving average (ARIMA) model. Here, we employed three statistical evaluation criteria in order to measure the performance of the applied models. The general conclusion that can be derived from this paper is that the usefulness of the ARFIMA model outperformed the existing ARIMA model. [ABSTRACT FROM PUBLISHER]

This paper utilized the Auto Regressive Integrated Moving Average (ARIMA) technique to forecast the near future rainfall using the long-term satellite rainfall data. At present, many of the future rainfall and climate forecast was conducted for a long-term period (>10 years) at the macroscale (∼1000 km and above) level. Those forecasts are inappropriate to meet the demand for the short-term action (1–3 years). In addition, the macroscale information is unsuitable and too general for the local administrative council and water resources stakeholders to make the adaptation plan, mitigation action, and preventive measures at smaller scale (10–50 km). Many developing countries were facing hydrological data conflict (scarcity, sparse, etc.) and utilizing the publicly accessed satellite data could be useful alternatives. Therefore, sound evidence is required to validate the hypothesis where the use of ARIMA and the long-term satellite rainfall data could provide near future rainfall forecast. Prior to that, the southern part of Johor in Malaysia is selected as the experimental site for this study. To perform the task, 204 of monthly gridded of satellite rainfall from 1998 to 2014 is utilized. In implementing the ARIMA, the standard seasonal lag based on local monsoon period is chosen. The auto-regression co-efficient is computed for each individual grid based on the time series of monthly rainfall, as well as the forecasted rainfall. To determine the ideal duration of historical rainfall for the best near-future forecasted rainfall, the rainfall datasets are divided into decadal and semi-decadal (epoch is every 5 years). The results showed that the satellite-based forecasted rainfall based on ARIMA (SAFORA) using half-decadal datasets able to forecast the 2015 rainfall at average temporal correlation of 0.703. Measurement biases range from 5 to 70%, depending on monsoon season. The SAFORA tends to overestimate low rainfall months. The output of this experiment proved that SAFORA able to forecast the monthly-scale rainfall at reliable accuracy and could be useful for hydrological data conflict situation catchment or location in the humid tropics. • The satellite forecasted rainfall using ARIMA was able to forecast the monthly rainfall with correlation of 0.703. • The best datasets to forecast near future of monthly rainfall for the study area is half-decade datasets. • Measurement bias range from 5 to 70% depending on monsoon season. • The satellite forecasted rainfall using ARIMA (SAFORA) tends to overestimate ground rainfall during low rainfall months. [ABSTRACT FROM AUTHOR]

This paper utilized the Auto Regressive Integrated Moving Average (ARIMA) technique to forecast the near future rainfall using the long-term satellite rainfall data. At present, many of the future rainfall and climate forecast was conducted for a long-term period (>10 years) at the macroscale (∼1000 km and above) level. Those forecasts are inappropriate to meet the demand for the short-term action (1–3 years). In addition, the macroscale information is unsuitable and too general for the local administrative council and water resources stakeholders to make the adaptation plan, mitigation action, and preventive measures at smaller scale (10–50 km). Many developing countries were facing hydrological data conflict (scarcity, sparse, etc.) and utilizing the publicly accessed satellite data could be useful alternatives. Therefore, sound evidence is required to validate the hypothesis where the use of ARIMA and the long-term satellite rainfall data could provide near future rainfall forecast. Prior to that, the southern part of Johor in Malaysia is selected as the experimental site for this study. To perform the task, 204 of monthly gridded of satellite rainfall from 1998 to 2014 is utilized. In implementing the ARIMA, the standard seasonal lag based on local monsoon period is chosen. The auto-regression co-efficient is computed for each individual grid based on the time series of monthly rainfall, as well as the forecasted rainfall. To determine the ideal duration of historical rainfall for the best near-future forecasted rainfall, the rainfall datasets are divided into decadal and semi-decadal (epoch is every 5 years). The results showed that the satellite-based forecasted rainfall based on ARIMA (SAFORA) using half-decadal datasets able to forecast the 2015 rainfall at average temporal correlation of 0.703. Measurement biases range from 5 to 70%, depending on monsoon season. The SAFORA tends to overestimate low rainfall months. The output of this experiment proved that SAFORA able to forecast the monthly-scale rainfall at reliable accuracy and could be useful for hydrological data conflict situation catchment or location in the humid tropics. • The satellite forecasted rainfall using ARIMA was able to forecast the monthly rainfall with correlation of 0.703. • The best datasets to forecast near future of monthly rainfall for the study area is half-decade datasets. • Measurement bias range from 5 to 70% depending on monsoon season. • The satellite forecasted rainfall using ARIMA (SAFORA) tends to overestimate ground rainfall during low rainfall months. [ABSTRACT FROM AUTHOR]