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Literature review presents a review of previous literature that has attempted to model and forecast exchange rate volatility using various ARCH and generalized autoregressive conditional heteroscedasticity (GARCH) models.
The primary objective of this paper is to model and forecast the United States presidential election via the usage of learning algorithms.
BPNNs' ability to model and forecast is not affected by the number of parameters but by the magnitude of the coefficients of the underlying structure.
Alizadeh and Mafinezhad (2010) used neural network models that utilized a predefined crisis variable to model and forecast Brent petrol prices and showed that the model is capable in forecasting both in normal and critical conditions.
This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model.
Therefore, as a result of detailed analysis of the processes involved from a systems approach, this paper contributes to the body of knowledge by proposing a mathematical framework to model and forecast supply chain fluidity when using any intermodal freight transport combination.
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This is a lower bound, since future improvements to models and forecast techniques, and larger ensembles, may yield higher correlations.
Threat-Based Security Engineering (TBSE) is a fresh approach to modelling and forecasting information security risk.
The models are utilized to model and to forecast the daily returns of crude oil prices.
For this reason, it is extremely important to properly model and forecast exchange rate volatility.
However, this is one of the hardest end-uses to analyze, model and forecast.
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