Regularised Gradient Boosting for Financial Time-series Modelling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Agapitos:2018:CMS,
-
author = "Alexandros Agapitos and Anthony Brabazon and
Michael O'Neill",
-
title = "Regularised Gradient Boosting for Financial
Time-series Modelling",
-
journal = "Computational Management Science",
-
year = "2017",
-
volume = "14",
-
number = "3",
-
pages = "367--391",
-
month = jul,
-
keywords = "genetic algorithms, genetic programming, Boosting
algorithms, Gradient boosting, Stagewise additive
modelling, Regularisation, Financial time-series
modelling, Financial forecasting, Feedforward neural
networks, ANN, Noisy data, Ensemble learning",
-
URL = "https://ideas.repec.org/a/spr/comgts/v14y2017i3d10.1007_s10287-017-0280-y.html",
-
DOI = "doi:10.1007/s10287-017-0280-y",
-
abstract = "Gradient Boosting (GB) learns an additive expansion of
simple basis-models. This is accomplished by
iteratively fitting an elementary model to the negative
gradient of a loss function with respect to the
expansion's values at each training data-point
evaluated at each iteration. For the case of
squared-error loss function, the negative gradient
takes the form of an ordinary residual for a given
training data-point. Studies have demonstrated that
running GB for hundreds of iterations can lead to
overfitting, while a number of authors showed that by
adding noise to the training data, generalisation is
impaired even with relatively few basis-models.
Regularisation is realised through the shrinkage of
every newly-added basis-model to the expansion. This
paper demonstrates that GB with shrinkage-based
regularisation is still prone to overfitting in noisy
datasets. We use a transformation based on a sigmoidal
function for reducing the influence of extreme values
in the residuals of a GB iteration without removing
them from the training set. This extension is built on
top of shrinkage-based regularisation. Simulations
using synthetic, noisy data show that the proposed
method slows-down overfitting and reduces the
generalisation error of regularised GB. The proposed
method is then applied to the inherently noisy domain
of financial time-series modelling. Results suggest
that for the majority of datasets the method
generalises better when compared against standard
regularised GB, as well as against a range of other
time-series modelling methods.",
-
notes = "School of Computer Science, University College Dublin,
Dublin, Ireland",
- }
Genetic Programming entries for
Alexandros Agapitos
Anthony Brabazon
Michael O'Neill
Citations