Online ridge regression method using sliding windows. Arce, P. & Salinas, L. C. In pages 87-90, 2013.
doi  abstract   bibtex   
A new regression method based on the aggregating algorithm for regression (AAR) is presented. The proposal shows how ridge regression can be modified in order to reduce the number of operations by avoiding the inverse matrix calculation only considering a sliding window of the last input values. This modification allows algorithm expression in a recursive way and therefore its use in an online context. Ridge regression, AAR and our proposal were compared using the closing stock prices of 45 stocks from the technology market from 2000 to 2012. Empirical results show that our proposal performs better than the other two methods in 28 of 45 stocks analyzed, due to the lower MSE error. © 2013 IEEE.
@inproceedings{10.1109/SCCC.2012.18,
    abstract = "A new regression method based on the aggregating algorithm for regression (AAR) is presented. The proposal shows how ridge regression can be modified in order to reduce the number of operations by avoiding the inverse matrix calculation only considering a sliding window of the last input values. This modification allows algorithm expression in a recursive way and therefore its use in an online context. Ridge regression, AAR and our proposal were compared using the closing stock prices of 45 stocks from the technology market from 2000 to 2012. Empirical results show that our proposal performs better than the other two methods in 28 of 45 stocks analyzed, due to the lower MSE error. © 2013 IEEE.",
    year = "2013",
    title = "Online ridge regression method using sliding windows",
    keywords = "Machine learning , Online learning , Ridge regression",
    pages = "87-90",
    doi = "10.1109/SCCC.2012.18",
    journal = "Proceedings - International Conference of the Chilean Computer Science Society, SCCC",
    author = "Arce, Paola and Salinas, Luís C."
}
Downloads: 0