Radwan E. Abdel-Aal

TL;DR: Autoregressive integrated moving average (ARIMA) models were developed using data for 5 yr and evaluated on forecasting new data for the sixth year as mentioned in this paper, and the optimum model derived is a multiplicative combination of seasonal and non-seasonal autoregressive parts, each being of the first order, following first differencing at both the seasonal and nonseasonal levels.

TL;DR: In this paper, abductive networks were used to forecast the next-hour wind speed from the previous 24 hourly measurements and an hour index, which gave an overall mean absolute error (MAE) of 0.83 between actual and predicted values.

TL;DR: Univariate modeling of the monthly demand time series based only on data for 6years is used to forecast the demand for the seventh year and is superior to [email protected]?ve forecasts based on persistence and seasonality, and is better than results quoted in the literature for several similar applications using multivariate abductive modeling, multiple regression, and univariate ARIMA analysis.

TL;DR: This paper proposes using the alternative technique of abductive networks, which offers the advantages of simplified and more automated model synthesis and analytical input-output models that automatically select influential inputs, provide better insight and explanations, and allow comparison with statistical and empirical models.

TL;DR: This work proposes using the alternative abductive networks approach, which offers the advantages of simplified and more automated model synthesis and transparent analytical input–output models, and compares favourably with neural network models developed using the same data.