The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. interest is increasing nowadays so efficient can greatly improve the performance these different applications. This paper presents novel approach which combines two models, Auto Regressive Moving Average (ARMA) Nonlinear with eXogenous input (NARX). ch...

Flooding is the most common natural disaster and continues to increase in frequency intensity due climate changes [7]. Currently, there a lack of efficient tools predict flooding. This research aimed create Time Series Machine Learning (ML) program using Auto Regressive Moving Average (ARIMA) models forecast streamflow, one prominent factors flood prediction. A streamflow dataset from Ganges Ri...

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, A...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...