The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by ...

Two Issini genera, Sarima Melichar, 1903 and Sarimodes Matsumura, 1916, are examined. One new Sarima species: Sarima bifurcus sp. n. and two new Sarimodes species Sarimodes clavatus sp. n. and Sarimodes parallelus sp. n. are added from South China. A checklist of species in the genus Sarima with data on distribution is provided. The distribution and morphological peculiarities of the genera Sar...

BACKGROUND Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, ou...

Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included...

Journal of Energy in Southern Africa • Vol 23 No 3 • August 2012 23 Abstract In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them wit...

Intelligent Transportation Systems (ITS) is an emerging concept which has been utilised to improve efficiency and sustainability of existing transportation systems. Short term traffic flow forecasting, the process of predicting future traffic conditions based on historical and realtime observations is an essential aspect of ITS. The existing well-known algorithms used for short-term traffic for...

OBJECTIVES To develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital. METHODS Data were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for v...

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and ...

This Article is brought to you for free and open access by the Ocean, Earth & Atmospheric Sciences at ODU Digital Commons. It has been accepted for inclusion in OEAS Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. Repository Citation Prista, Nuno; Diawara, Norou; Costa, Maria J.; and Jones, Cynthia M., "Use...

BACKGROUND Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China. METHODS Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Heal...