Air pollution is a worldwide issue that affects the lives of many people in urban areas. It considered air may lead to heart and lung diseases. A careful timely forecast quality could help reduce exposure risk for affected people. In this paper, we use data-driven approach predict based on historical data. We compare three popular methods time series prediction: Exponential Smoothing (ES), Auto...

Forecasting accuracy is one of the most favorable critical issues in Autoregressive Integrated Moving Average (ARIMA) models. The study compares the application of two forecasting methods on the amount of Taiwan export, the Fuzzy time series method and ARIMA method. Model discussed for the ARIMA method and Fuzzy time series method include the Sturges rules. When the sample period is extend in o...

In this study, the situation of Iran, U.S and Turkey's Pistachio export is investigated. to this purpose, Revealed Comparative Advantage (RCA) Index is calculated based on Agricultural and total economy export, separately, then forecasted by using Auto- Regressive Integrated Moving Average (ARIMA) approached, for 2008-2013. The results show that considering both commodity baskets, Turkey and Ir...

Predicting stock price is a trend yet very challenging task. It because the prices depend upon several internal and external factors. Stock prediction can be useful for financial sectors government help in informed decision-making. This paper analyzes market of K-Electric Karachi. found that K-electric on refinery sector. The data two sectors. Also, compares based moving average, auto-regressiv...

In the mining sector, the barrier to obtain an efficient safety management system is the unavailability of future information regarding the accidents. This paper aims to use the auto-regressive integrated moving average (ARIMA) model, for the first time, to evaluate the underlying causes that affect the safety management system corresponding to the number of accidents and fatalities in the surf...

Flow discharge data must be available in a time series and accurate manner, so there should no empty periods. Therefore, model is needed that can reconstruct or estimate the flow of period stochastically. One way to solve this problem by filling generation.The philosophy create new sets based on generally incomplete short historical obtain longer more complete data. The long made with propertie...