Algorithm 2: Time-Window Process
Data loading:		{Inputs, Targets} → {Model’s Forecast, Observations}
Data normalization:		{Inputs, Targets} → [−1,1]
Determination of the Training and Testing data sets		Concerns the training process of the dual filter (KF and RBF) and the evaluation of the method
Determine the time window’s maximum number:		$Max\_tw=TimeWindow-1$
Set the appropriate matrices and vectors for storing the outcomes
Determine the set of penalty parameters:		$\mathrm{Penalty}\mathrm{Parameter}\lambda$$\to [{10}^{-6},{10}^{-2}]$
Determine the set of Clusters for the RBF network:		Cluster → [10:step:70]
Determine the set of activation functions:		Activation function → {Gaussian, Multiquadric}
	for $qq=0,\dots ,Max\_tw$
	$\hspace{1em}\hspace{1em}Tr$ →($1+qq):TrainingData+qq$	Training data for the RBFNN.% One step in time.
	Run the Algorithm 1	Obtain the optimal RBF structure. Centroids, widths, external weights, activation fun.
	${\hspace{1em}\hspace{1em}Tr}_1$ → $Tr\left(end\right)+1:Tr\left(end\right)+\mathrm{T}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{D}\mathrm{a}\mathrm{t}\mathrm{a}$	Testing data for the improved forecasts and the evaluation.
	Denormalize the data	Improved forecasts, corresponding model forecasts, and recorded observations.
	Assess the method based on ${Tr}_1$ and store the results	Bias, Absolute Bias, Rmse, and Ns indices.
	End
Save results from each time window