BACKGROUND China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic...

Natural calamities (e.g., hurricane, excessive ice-fall) may often impede the inventory replenishment during the peak sale season. Due to the extreme situations, sales may not occur and demand may not be recorded. This study focuses on forecasting of intermittent seasonal demand by taking random demand with a proportion of zero values in the peak sale season. Demand pattern for a regular time i...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

Measurements of high-speed network traffic have shown that traffic data exhibits a high degree of self-similarity. Traditional traffic models such as AR and ARMA are not able to capture this long-range-dependence making them ineffective for the traffic prediction task. In this paper, we apply the fractional ARIMA (F-ARIMA) model to predict one-step-ahead traffic value at different time scales. ...

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effecti...

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by (k) = e ? p k than by (k) = k ? = e ? log k (long-range dependence) or (k) = e ?k (Markovian). A video model with such a correlation structure is introduced based on the so-called M=G=1 input processes. Though not Markovian, the model exhibits short-range dependence. Using the qu...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by p(k) = e–~fi than by p(k) = k–fi = e–~’og k (long-range dependence) or p(k) = e-~k (Markovian). A video model with such a correlation structure is introduced based on the so-called M/G/ca input processes. Though not Markovian, the model exhibits short-range dependence. Using the ...

A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract fr...

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investi...