ftsa (version 4.2)

ftsa-package: Functional Time Series Analysis

Description

This package presents descriptive statistics for modeling functional data; implements principal component regression and partial least squares regression to provide point and distributional forecasts for functional data; utilizes ordinary least squares, penalized least squares, ridge regression, and moving block approaches to dynamically update point and distributional forecasts when partial data points in the most recent curve are observed.

docType

package

References

R. J. Hyndman and H. L. Shang (2009) "Forecasting functional time series (with discussion)", Journal of the Korean Statistical Society, 38(3), 199-221.

R. J. Hyndman and H. L. Shang (2010) "Rainbow plots, bagplots, and boxplots for functional data", Journal of Computational and Graphical Statistics, 19(1), 29-45.

H. L. Shang and R. J. Hyndman (2011) "Nonparametric time series forecasting with dynamic updating", Mathematics and Computers in Simulation, 81(7), 1310-1324.

H. L. Shang, H. Booth and R. J. Hyndman (2011) "Point and interval forecasts of mortality rates and life expectancy: a comparison of ten principal component methods, Demographic Research, 25(5), 173-214.

H. L. Shang (2011) "rainbow: an R package for visualizing functional time series, The R Journal, 3(2), 54-59.

H. L. Shang (2012) "Point and interval forecasts of age-specific fertility rates: a comparison of functional principal component methods", Journal of Population Research, 29(3), 249-267.

H. L. Shang (2012) "Point and interval forecasts of age-specific life expectancies: a model averaging", Demographic Research, 27, 593-644.

H. L. Shang (2013) "Functional time series approach for forecasting very short-term electricity demand", Journal of Applied Statistics, 40(1), 152-168.