Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barom...

Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...

A Novel Divide-and-Conquer Model for CPI Prediction Using ARIMA, Gray Model and BPNN

A Multivariate Arima Model to Forecast Air Transport Demand

Forecast of air transport demand has a great influence on the development of airport master plans with respect both to airside (runways, taxiways, aprons, technological devices) and landside (boarding/landing area, waiting rooms, etc.), given that it depends on the amount of passengers during the reference time period, usually the year or more years for such aim. As a result of deregulation and...

Traffic Modeling and Prediction using ARIMA/GARCH Model

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...