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The PV generation is predicted using an auto regressive moving average (ARMA) time series model.
The authors introduced auto regressive moving average technique for controlled charging of electrical vehicles [48].
In [15], authors presented two run time estimation methods named: "auto regressive moving average (ARMA)" and "Kalman Filters".
Time series estimators include Auto Regressive Moving Average (ARMA) [58], Smoothing Spline [59], Kalman Filter [60] and Fast Fourier Transform [61].
An auto regressive moving average (ARMA) model ARMA p, q) with appropriate order can be used to describe the stationary stochastic process of power load.
Afterwards, comparing WT-ANN, ANN, and auto regressive moving average (ARMA) methods revealed that WT-ANN can significantly reduce the error in spite of ANN and ARMA methods.
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Regression methods, including vector auto-regression model, vector auto-regressive moving average model.
Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models.
which reveals the auto-regressive moving average (ARMA) structure (order (k,k)) of the observations y r as a random process.
Instead of a complex first-principles model, a polynomial auto-regressive moving average model (ARMA) is used to describe the nonlinear behavior of the polymerization reactor.
It uses a special case of first-order auto-regressive moving average filter to integrate the filtered difference as another way of handover decision for real-time services.
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