| funtimes-package {funtimes} | R Documentation |
funtimes: Functions for Time Series Analysis
Description
Advances in multiple aspects of time-series analysis are documented in this package.
See available vignettes using
browseVignettes(package = "funtimes")
Tests for trends applicable to autocorrelated data, see
vignette("trendtests", package = "funtimes")
include bootstrapped versions of t-test and Mann–Kendall test (Noguchi et al. 2011)
and bootstrapped version of WAVK test for possibly non-monotonic trends (Lyubchich et al. 2013).
The WAVK test is further applied in testing synchronicity of trends (Lyubchich and Gel 2016);
see an implementation to climate data in Lyubchich (2016).
With iterative testing, the synchronicity test is also applied for identifying clusters of
multiple time series (Ghahari et al. 2017).
Additional clustering methods are implemented using functions BICC (Schaeffer et al. 2016)
and DR (Huang et al. 2018);
function purity can be used to assess the accuracy of clustering if true classes are known.
Changepoint detection methods include modified CUSUM-based bootstrapped test (Lyubchich et al. 2020).
Additional functions include implementation of the Beale's ratio estimator, see
vignette("beales", package = "funtimes")
Nonparametric comparison of tails of distributions is implemented using small bins defined based on
quantiles (Soliman et al. 2015)
or intervals in the units in which the data are recorded (Lyubchich and Gel 2017).
For a list of currently deprecated functions, use ?'funtimes-deprecated'
For a list of defunct (removed) functions, use ?'funtimes-defunct'
Author(s)
Maintainer: Vyacheslav Lyubchich lyubchich@umces.edu (ORCID)
Authors:
Yulia R. Gel
Srishti Vishwakarma
Other contributors:
Alexander Brenning [contributor]
Calvin Chu [contributor]
Xin Huang [contributor]
Umar Islambekov [contributor]
Palina Niamkova [contributor]
Dorcas Ofori-Boateng [contributor]
Ethan D. Schaeffer [contributor]
Xingyu Wang [contributor]
References
Ghahari A, Gel YR, Lyubchich V, Chun Y, Uribe D (2017).
“On employing multi-resolution weather data in crop insurance.”
In Proceedings of the SIAM International Conference on Data Mining (SDM17) Workshop on Mining Big Data in Climate and Environment (MBDCE 2017).
Huang X, Iliev IR, Lyubchich V, Gel YR (2018).
“Riding down the bay: space-time clustering of ecological trends.”
Environmetrics, 29(5–6), e2455.
doi:10.1002/env.2455.
Lyubchich V (2016).
“Detecting time series trends and their synchronization in climate data.”
Intelligence. Innovations. Investments, 12, 132–137.
Lyubchich V, Gel YR (2016).
“A local factor nonparametric test for trend synchronism in multiple time series.”
Journal of Multivariate Analysis, 150, 91–104.
doi:10.1016/j.jmva.2016.05.004.
Lyubchich V, Gel YR (2017).
“Can we weather proof our insurance?”
Environmetrics, 28(2), e2433.
doi:10.1002/env.2433.
Lyubchich V, Gel YR, El-Shaarawi A (2013).
“On detecting non-monotonic trends in environmental time series: a fusion of local regression and bootstrap.”
Environmetrics, 24(4), 209–226.
doi:10.1002/env.2212.
Lyubchich V, Lebedeva TV, Testa JM (2020).
“A data-driven approach to detecting change points in linear regression models.”
Environmetrics, 31(1), e2591.
doi:10.1002/env.2591.
Noguchi K, Gel YR, Duguay CR (2011).
“Bootstrap-based tests for trends in hydrological time series, with application to ice phenology data.”
Journal of Hydrology, 410(3), 150–161.
doi:10.1016/j.jhydrol.2011.09.008.
Schaeffer ED, Testa JM, Gel YR, Lyubchich V (2016).
“On information criteria for dynamic spatio-temporal clustering.”
In Banerjee A, Ding W, Dy JG, Lyubchich V, Rhines A (eds.), The 6th International Workshop on Climate Informatics: CI2016, 5–8.
doi:10.5065/D6K072N6.
Soliman M, Lyubchich V, Gel YR, Naser D, Esterby S (2015).
“Evaluating the impact of climate change on dynamics of house insurance claims.”
In Lakshmanan V, Gilleland E, McGovern A, Tingley M (eds.), Machine Learning and Data Mining Approaches to Climate Science, chapter 16, 175–183.
Springer, Switzerland.
doi:10.1007/978-3-319-17220-0_16.