TrendLSW (version 1.0.6)

TrendLSW-package: 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.

Author

Euan T. McGonigle <[e-mail]>, Rebecca Killick <[e-mail]>, and Matthew Nunes <[e-mail]>

Maintainer: Euan T. McGonigle <[e-mail]>

Details

Package:	TrendLSW
Type:	Package
Version:	1.0.0
Date:	2024-04-17
License:	GPL
LazyLoad:	yes

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

TLSW,TLSWsim,plot.TLSW

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)