The intermittent nature of the wind creates significant uncertainty in the operation of power systems with increased wind power penetration. Considerable efforts have been made for the accurate prediction of the wind power using either statistical or physical models. In this paper, a method based on Artificial Neural Network (ANN) is proposed in order to improve the predictions of an existing n...

A large literature in exchange rate economics has investigated the forecasting performance of empirical exchange rate models using conventional point forecast accuracy criteria. However, in the context of managing exchange rate risk, interest centers on more than just point forecasts. This paper provides a formal evaluation of recent exchange rate models based on the term structure of forward e...

Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive dif%culties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline 4eets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and ...

BACKGROUND Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages...

The accurate forecasting of carbon dioxide (CO2) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the di...

Back Propagation (BP) neural network, Widely adopted and utilized in automatic control, image recognition, hydrological forecasting and water quality evaluation, etc., as one of the Artificial Neural Networks, has stronger function and property of mapping, classification, functional fitting. This article takes the water flow of Lanzhou section of Yellow river in China as an example by the way o...

Analyzing mass information and supporting insight based on analysis results are very important work but it needs much effort and time. Therefore, in this paper, we propose an approach for predicting hype cycle based on machine learning for effective, systematic, and objective information analysis and future forecasting of science and IT field. Additionally, we execute a comparative evaluation b...

For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this pape...

The analysis proceeds with a review of the problem of forecasting transportation requirements. Several idiosyncratic models are examined and their properties specified. The forecasting of transportation stocks and commodity flows is discussed. Models of the demand for transportation services are implemented with empirical data. Graph theory as an interpretative frame of reference is introduced ...