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.
To install TrendLSW
from GitHub:
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 + noise
Apply the TLSW
function:
x.TLSW <- TLSW(x)
Visualise the estimated trend and spectrum:
plot(x.TLSW)