Regularised Gradient Boosting for Financial Time-series Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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