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We design and implement a Bayesian network model to forecast daily ozone states.
Campbell and Diebold [6] forecast daily average temperature using a nonstructural time-series approach.
This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand.
We design new variable in the warning model, precisely forecast daily price changes of agricultural commodities in a large scale.
OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1 3 days.
A support vector machine (SVM) model considering the impacts of oil and natural gas prices is proposed in [12] to forecast daily electricity price.
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An empirical application is provided for forecasting daily returns in gold market.
These data permit validation of the UV index, a measure of solar risk to UV exposure, forecasted daily by CONAE Argentina National Commission on Space Activitiess) and the Argentine National Weather Service.
Open image in new window Fig. 4 Forecasted generation curves Open image in new window Fig. 5 Forecasted daily load curves.
The purpose of this work is to explore the possibility of forecasting daily maximum ozone impacts in urban areas of Kuwait, where most measurements are taken at surface stations.
The presented study comprised the employment of this seldom used ANN method, generalized regression neural network (GRNN), in comparison to both a frequently applied neural network training algorithm, feed-forward back-propagation (FFBP), and a stochastic model of auto-regressive structure for the purpose of forecasting daily trip flows, which is an essential component in demand analysis.
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