TrendLSW - Wavelet Methods for Analysing Locally Stationary Time Series
Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will likely want to start with the TLSW function.
Last updated 11 months ago
nonparametric-regressionspectral-analysisspectrumtime-seriestime-series-analysiswavelets
3.60 score 1 stars 3 scripts 116 downloadsCptNonPar - Nonparametric Change Point Detection for Multivariate Time Series
Implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2023) <doi:10.48550/arXiv.2305.07581> for description of the NP-MOJO methodology.
Last updated 11 months ago
change-point-detectionmoving-sumnonparametricsegmentationtime-seriescpp
3.60 score 4 stars 4 scripts 708 downloads