This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second ha...

Abstract Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter's exponential smoothing model, have been inv...

in this study, we focused on tehran stock exchange market analysis based on applying moving average rules. the tehran stock exchange in the middle east has evolved into an exciting and growing marketplace where individual and institutional investor trade securities of over 420 companies. in an attempt to examine the ability to earn excess return by exploiting moving average rules, the average a...

When the classical H∞ algorithm (HIF) is applied to estimate state of charge (SOC) a lithium battery, influence historical data usually ignored, resulting in an increase estimation error. In order improve accuracy SOC estimation, this paper proposes extended exponential weighted moving average (EE-HIF) view data. By designing Gaussian function, distribution at different times can effectively re...

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...