BACKGROUND Using 7-year population-based data on Taiwan, we examined seasonal variation in violent versus nonviolent suicide, and its association with meteorological factors: ambient temperature, relative humidity, atmospheric pressure, rainfall and daily sunshine hours. METHODS We used Taiwan's nationwide mortality data from 1997 to 2003, categorizing the sample decedents into two groups, vi...

The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode d...

The outbreak of the COVID-19 has brought about huge economic loss and civil aviation industries all over world have suffered severe damage. An effective method is urgently needed to accurately predict air-transport demand under influences such accidental factors. This paper proposes a novel predicting framework for considering uncertainties caused by factors including regional wars, climatic an...

Background Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that associated with high morbidity. Reliable forecasting crucial for prevention and control. Recently, hybrid models have become popular, wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based models. Thus, our aim to forecast number HFMD cases Mater...

TIn this paper, the Chilean tourism demand experienced during period 2000?2018 has been modelled using a linear regression model with dichotomous variables (MRL) and an ARIMA seasonal component (SARIMA). The results show that SARIMA approach is more effective in replicating non?stationary, non?linear behaviour presence of seasonality series, forecasts obtained from presenting low rate error, ca...

Abstract This study suggests an ultra-short-term photovoltaic (PV) energy generation forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Machine (SVM). It can further increase the predictive performance of PV electricity electrical output address issue significant fluctuation instability power power. To achieve combined prediction, SVM is emp...

Now-a-days, different sectors of the economy are being significantly affected by the electricity variable. In this research, we analyzed the monthly electricity consumption in Pakistan for the period of January 1990 through December 2011, using linear and non linear modeling techniques. They include ARIMA, Seasonal ARIMA (SARIMA) and ARCH/GARCH models. Electricity consumption model reveals a si...

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series dat...