Optimal Deseasonalization for Monthly and Daily Geophysical Time Series

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors A. Ian McLeod, Hyukjun Gweon
Journal/Conference Name Journal of Environmental Statistics
Paper Category
Paper Abstract Deseasonalized geophysical time series are often used in time series models (Hipel and McLeod 1994). In this article an optimal method for selecting the deseasonalization transformation is suggested and an R package implementation (McLeod and Gweon 2012) is discussed. Our deseasonalization method may be used with the recently developed periodic autoregression model for daily river flow suggested by Tesfaye, Anderson, and Meerschaert (2011) and for the hierarchical Bayes modeling for multi-site daily temperature series discussed by Craigmile and Guttorp (2011).
Date of publication 2012
Code Programming Language R
Comment

Copyright Researcher 2021