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Forecasting capabilities of neural network techniques are promising.
The aim of the work is to develop numerical finite element models and simulation techniques to be implemented in a numerical procedure, which is aimed to improve designer forecasting capabilities of damaging resistance in composite structures.
An evaluation, in terms of forecasting capabilities, of three ionospheric time delay models – an Auto Regressive Moving Average (ARMA) model, Auto Regressive Integrated Moving Average (ARIMA) model, and a Holt-Winter's model is presented.
It was demonstrated that separating streamflow into different components such as base flow and surface flow can be useful for improving simulation and forecasting capabilities of machine learning models.
We present a Maple implementation of the well known global approach to time series analysis and some further developments designed to improve the computational efficiency of the forecasting capabilities of the approach.
Moreover, very few attempts have been made to compare the forecasting capabilities of different models.
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Evaluation indicators further confirmed the high forecasting capability of the trained ANN models.
The results pointed to the good forecasting capability of the approach, which repeatedly outperformed the "Buy and Hold" strategy.
Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time.
The results demonstrate excellent forecasting capability of the SaDE-ELM model in respect to the nine benchmark models.
The quality of flood forecasting capability of the model was accessed using four accuracy measures (probability of false detection, false alarm ratio, critical success index, and probability of detection) for three return periods 1.005, 1.05 and 1.25.
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CEO of Professional Science Editing for Scientists @ prosciediting.com