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In this study, using 10 years' monthly values of Currency Issued (CI), Total Money Supply (TMS, Consumerr Price Index (CPI) and Interest Rate (IR), by means of Adaptive Neuro-Fuzzy Inference System, we have created a forecast model MFDD (Model of Forecasting the Domestic Debt) in order to predict domestic debt.
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This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts.
Empirical survey data are necessary to estimate behavioural models capable of forecasting stakeholders' reactions to alternative policies based on agents' stated preferences [18, 19, 20, 21].
The issue of how to create a dynamic data-dependent forecast model of a threshold forecast system, whose threshold changes according to the change of planting environment has been solved.
A comprehensive model for forecasting the number of interruptions based on the weather condition is proposed.
Results indicate that the proposed DE LSTM model outperforms existing forecasting models in terms of forecasting accuracies.
The neural network infill drilling recovery model is capable of forecasting the oil recovery with less error variance.
We fitted regression models for incidences of dengue cases using 1996-2004 data from each province and evaluated the ability of fitted models to forecast out-of-fit data in the year 2005.
In spite of the impressive diversity of models of fuzzy forecasting, there is still a burning need to arrive at models that are both accurate and highly interpretable.
Then, the distribution characteristic of wind power forecasting errors can be obtained by the model, and the probability distribution model of wind power forecasting errors will be given.
A probability distribution model of wind power forecasting errors and a mathematic expectation of the power shortage caused by forecasting errors are established.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com