We consider the forecasting of cointegrated variables, and we show that at long horizons nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to v...

This paper presents a simultaneous model of exchange rates between the three major countries. In addition to incorporating long-run equilibria and short-run dynamics, the model is designed to capture complex interactions between currencies not normally considered in exchange rate models. These interactions are shown to be important via generalised impulse response analysis, and the model as a w...

BACKGROUND Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpa...

The growing interest in renewable energy and the falling prices of solar panels place electricity a favourable position for adoption. However, high-rate adoption intermittent introduces challenges potential to create power instability between available generation load demand. Hence, accurate Photovoltaic (PV) forecasting is essential maintain system reliability maximize integration. current PV ...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound int...

review and classification of electric load forecasting (lf) techniques based on artificial neuralnetworks (ann) is presented. a basic anns architectures used in lf reviewed. a wide range of annoriented applications for forecasting are given in the literature. these are classified into five groups:(1) anns in short-term lf, (2) anns in mid-term lf, (3) anns in long-term lf, (4) hybrid anns inlf,...

This work presents an alternative metric for evaluating the quality of solar forecasting models. Some conventional approaches use quantities such as the root-mean-squareerror (RMSE) and/or correlation coefficients to evaluate model quality. The direct use of statistical quantities to assign forecasting quality can be misleading because these metrics do not convey a measure of the variability of...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...

in recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. for solving this problem, some approaches have been presented. recently, the artificial neural networks (ann) as a heuristic method has more applications for this propose. in this paper, short-term (1 hour) and mid-term (24...