Low-dimensional decomposition, smoothing and forecasting of sparse functional data

 

We propose a new generic method ROPES (Regularized Optimization for Prediction and Estimation with Sparse data) for decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the LASSO, Principal Component Analysis (PCA) and Maximum-Margin Matrix Factorisation (MMMF). Using this new approach, we propose a practical method of forecasting mortality rates, as well as a new method for interpolating and extrapolating sparse longitudinal data. We also show how to calculate prediction intervals for the resulting estimates.

Cite

@techreport{ropes,
  author      = {Alexander Dokumentov and Rob J Hyndman},
  institution = {Department of Econometrics \& Business Statistics, Monash University},
  number      = {16/14},
  title       = {Low-dimensional decomposition, smoothing and forecasting of sparse functional data},
  type        = {Working paper},
  url         = {http://robjhyndman.com/publications/ropes/},
  year        = 2014
}