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In one of the cases, optimization over multiple history-matched models provides significant improvement over optimizing with a single model.
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The models provide significant theoretical basis to airline flight rescheduling.
Compared to the LSTAR-LST-GARCH models, the models provided significant improvement.
These models provide significant insights, and yet they do not explain the actual operation of venture capital.
Yu et al. (2007) followed neural network modeling methods to forecast crude oil prices and showed that NN models provide significant gains in terms of different error criteria.
The results also showed that the LSTAR-LST-GARCH-RBF and LSTAR-LST-GARCH-MLP models provided significant gains in modeling petrol prices and in forecasting out-of-sample oil price returns.
The results showed that the LSTAR based and neural network augmented models provided significant gains in terms of modeling the daily returns of oil prices when compared with the results of the baseline GARCH family models.
Results supported the following conclusions for modeling and forecasting volatility in crude oil prices: (i) The nonlinear volatility models with STAR type nonlinearity namely, LSTAR-LST-GARCH family provided significant gains in terms of in-sample (one-step-ahead) forecasting accuracy and these models provided significant improvement over their single-regime GARCH variants.
Because of their parsimony, the skeleton models provide significant insight into the logic of these processes.
Moreover, inclusion of meteorological predictors into both autoregressive models provided significant forecast skill improvement during the most critical situations (during severe dengue outbreaks) compared with their autoregressive counterparts without meteorological predictors.
The use of precise CCI model provides significant improvement in the performance analysis.
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