| TrendLSW-package {TrendLSW} | R Documentation |
Wavelet Methods for Analysing Locally Stationary Time Series
Description
Provides wavelet-based methods for trend, spectrum and autocovariance estimation of locally
stationary time series. See TLSW for the main estimation function.
Details
| Package: | TrendLSW |
| Type: | Package |
| Version: | 1.0.0 |
| Date: | 2024-04-17 |
| License: | GPL |
| LazyLoad: | yes |
Author(s)
Euan T. McGonigle <e.t.mcgonigle@soton.ac.uk>, Rebecca Killick <r.killick@lancs.ac.uk>, and Matthew Nunes <m.a.nunes@bath.ac.uk>
Maintainer: Euan T. McGonigle <e.t.mcgonigle@soton.ac.uk>
References
Spectral estimation with differencing/nonlinear trend estimator: 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.
Spectral estimation in presence of trend/linear trend estimator: McGonigle, E. T., Killick, R., and Nunes, M. (2022). Trend locally stationary wavelet processes. Journal of Time Series Analysis, 43(6), 895-917.
LSW processes without trend: Nason, G. P., von Sachs, R., and Kroisandt, G. (2000). Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(2), 271–292.
lacf estimation without trend: Nason, G. P. (2013). A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(5), 879–904.
See Also
Examples
# simulates an example time series and estimates its trend and evolutionary wavelet spectrum
spec <- matrix(0, nrow = 9, ncol = 512)
spec[1,] <- 1 + sin(seq(from = 0, to = 2 * pi, length = 512))^2
trend <- seq(from = 0, to = 5, length = 512)
set.seed(1)
x <- TLSWsim(trend = trend, spec = spec)
x.TLSW <- TLSW(x)
summary(x.TLSW)
plot(x.TLSW)