Title :
Online Ridge Regression Method Using Sliding Windows
Author :
Arce, Pedro ; Salinas, Luis
Author_Institution :
Center for Technol. Innovation in High Performance Comput., UTFSM, Valparaiso, Chile
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.
Keywords :
financial data processing; learning (artificial intelligence); mathematics computing; matrix inversion; regression analysis; AAR; aggregating algorithm-for-regression; closing stock prices; inverse matrix calculation avoidance; machine learning; online learning; online ridge regression method; sliding windows; Context; Equations; Mathematical model; Prediction algorithms; Predictive models; Proposals; Vectors; Machine Learning; Online Learning; Ridge Regression;
Conference_Titel :
Chilean Computer Science Society (SCCC), 2012 31st International Conference of the
Conference_Location :
Valparaiso
Print_ISBN :
978-1-4799-2937-5
DOI :
10.1109/SCCC.2012.18
