The kernel recursive least squares tracker (KRLS-T) is a model proposed by Vaerenbergh et al. [1].
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KRLS-T is the KRLS-T 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] S. Van Vaerenbergh, M. L ́azaro-Gredilla, I. Santamar ́ıa, Kernel recursive least-squares tracker for time-varying regression, IEEE Transactions on Neural Networks and Learning Systems 23 (8) (2012) 1313–1326. doi: https://doi.org/10.1109/TNNLS.2012.2200500.