The kernel recursive least squares (KRLS) is a model proposed by Engel et al. [1] based on the recursive least squares (RLS) algorithm, that implements the Gaussian kernel to cope with linearities in the data.
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KRLS is the KRLS model.
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GridSearch_AllDatasets is the file to perform a grid search for all datasets and store the best hyper-parameters.
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Runtime_AllDatasets perform 30 simulations for each dataset and compute the mean runtime and the standard deviation.
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MackeyGlass is the script to prepare the Mackey-Glass time series, perform simulations, compute the results and plot the graphics.
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Nonlinear is the script to prepare the nonlinear dynamic system identification time series, perform simulations, compute the results and plot the graphics.
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LorenzAttractor is the script to prepare the Lorenz Attractor time series, perform simulations, compute the results and plot the graphics.
[1] Engel, S. Mannor, R. Meir, The kernel recursive least-squares algorithm, IEEE Transactions on Signal Processing 52 (8) (2004) 2275–2285. https://doi.org/10.1109/tsp.2004.830985