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      Joint optimization of linear and nonlinear models for sequential regression

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      Author(s)
      Fazla, Arda
      Aydin, Mustafa E.
      Kozat, Suleyman S.
      Date
      2022-12
      Source Title
      Digital Signal Processing
      Print ISSN
      1051-2004
      Electronic ISSN
      1095-4333
      Publisher
      Academic Press
      Volume
      132
      Pages
      103802-1 - 103802-11
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      We investigate nonlinear regression and introduce a novel approach based on the joint optimization of linear and nonlinear models. In order to capture both the nonlinear and linear characteristics in sequential data, we model the underlying data as a combination of linear and nonlinear models, where we optimize the models jointly to minimize the final regression error. As the nonlinear model, we employ a differentiable version of the boosted decision trees. As the linear model, we use the well-known SARIMAX model. Our approach is generic so that any differentiable nonlinear or linear model can be readily employed provided that they are differentiable. By this joint optimization, we alleviate the well-known underfitting and overfitting problems in modeling sequential data. Through our experiments on synthetic and real-life data, we demonstrate significant improvements over individual components as well as the combination/mixture methods in the literature.
      Keywords
      Online learning
      Regression
      Seasonal auto-regressive integrated moving average with eXogenous factors (SARIMAX)
      Soft gradient boosting decision tree (Soft GBDT)
      Stochastic gradient descent (SGD)
      Permalink
      http://hdl.handle.net/11693/111460
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.dsp.2022.103802
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      • Department of Electrical and Electronics Engineering 4016
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