• Corpus ID: 88524836

Time Series Analysis of the Southern Oscillation Index using Bayesian Additive Regression Trees

  title={Time Series Analysis of the Southern Oscillation Index using Bayesian Additive Regression Trees},
  author={Sean van der Merwe},
  journal={arXiv: Applications},
Bayesian additive regression trees (BART) is a regression technique developed by Chipman et al. (2008). Its usefulness in standard regression settings has been clearly demonstrated, but it has not been applied to time series analysis as yet. We discuss the difficulties in applying this technique to time series analysis and demonstrate its superior predictive capabilities in the case of a well know time series: the Southern Oscillation Index. 

Adaptive bayesian sum of trees model for covariate dependent spectral analysis.

The proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates and is used to study gait maturation in young children by evaluating age-related changes in power spectra of stride interval time series in the presence of other covariates.



BART: Bayesian Additive Regression Trees

We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian

Predicting losses due to spillage at the Gariep hydro-power plant

  • Report to ESKOM,
  • 2009

Modelling and forecasting the southern oscillation : A time - domain approach

  • Monthly Weather Review
  • 1985

It is worth noting that this model produces white noise residuals when making onemonth-ahead predictions. This can be seen quite clearly in the correlogram below: References Chipman

  • Bayesian additive regression trees
  • 2008

Modelling and forecasting the southern oscillation: A timedomain approach, Monthly Weather Review 113(11),pp.1876-1888

  • 1985