In order to improve the safety of train operation, a short-term wind speed forecasting method is proposed based on a linear recursive autoregressive integrated moving average (ARIMA) algorithm and a non-linear recursive generalized autoregressive conditionally heteroscedastic (GARCH) algorithm (ARIMA-GARCH). Firstly, the non-stationarity embedded in the original wind speed data is pre-processed...

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose...

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose...

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mini...

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...

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average w...

This article attempts to present a basic method of time series analysis, modelling and forecasting performance of ARIMA, GARCH (1,1) and mixed ARIMA GARCH (1,1) models using historical daily close price downloaded through the yahoo finance website from the NASDAQ stock exchange for GE company (USA) during the period of 2001 to 2014. This paper also presents a brief analysis technique introducti...

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1),GARCH(1,1), EGARCH(1,1) and ARIMA-...

It is meaningful and of certain theoretical value for the development economy through analyzing fluctuation rules international oil prices forecasting future trend prices. By composing autoregressive integrated moving average (ARIMA) model combination model-generalized conditional heteroskedasticity (ARIMA-GARCH) prices, study shows that ARIMA (1,1,0)-GARCH (1,1) more suitable short-term with h...

We identify and estimate the mean and variance components of the daily closing share prices using ARIMA-GARCH type models by explaining the volatility structure of the residuals obtained under the best suited mean models for the said series. The parameters of ARIMA type simple specifications are routinely anticipated by applying the OLS methodology but it has two disadvantages when the volatili...