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Implements wavelet methods for analysis of nonstationary time series. See
McGonigle, E. T., Killick, R., and Nunes, M. (2022). Trend locally stationary wavelet processes. Journal of Time Series Analysis, 43(6), 895-917.
McGonigle, E. T., Killick, R., and Nunes, M. (2022). Modelling time-varying first and second-order structure of time series via wavelets and differencing. Electronic Journal of Statistics, 6(2), 4398-4448.
for full details.
You can install the released version of TrendLSW from CRAN with:
install.packages("TrendLSW")You can install the development version of TrendLSW from GitHub with:
devtools::install_github("https://github.com/EuanMcGonigle/TrendLSW")For detailed examples, see the help files within the package. We can generate a small example for performing trend and spectrum estimation as follows:
library(TrendLSW)
set.seed(1)
noise <- rnorm(512) * c(seq(from = 1, to = 3, length = 256), seq(from = 3, to = 1, length = 256))
trend <- seq(from = 0, to = 5, length = 512)
x <- trend + noiseApply the TLSW function:
Visualise the estimated trend and spectrum:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.