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Low-dimensional decomposition, smoothing and forecasting of sparse functional data
journal contributionposted on 2022-11-09, 00:30 authored by Alexander Dokumentov, Rob J Hyndman
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.
Classification-JELC10, C14, C33
Working Paper Series Number16/14