mvLSWimpute-package: Imputation Methods for Multivariate Locally Stationary Time...

In mvLSWimpute: Imputation Methods for Multivariate Locally Stationary Time Series

Imputation Methods for Multivariate Locally Stationary Time Series

Description

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

Details

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The main routine of the package is mv_impute which performs forward or forward and backward imputation of locally stationary multivariate time series, using one-step ahead forecasting (and backcasting).

Author(s)

Rebecca Wilson [aut], Matt Nunes [aut, cre], Idris Eckley [ctb, ths], Tim Park [ctb]

Maintainer: Matt Nunes <[e-mail]>

References

Wilson, R. E., Eckley, I. A., Nunes, M. A. and Park, T. (2021) A wavelet-based approach for imputation in nonstationary multivariate time series. _Statistics and Computing_ *31* Article 18, doi:10.1007/s11222-021-09998-2.

mvLSWimpute documentation built on Aug. 16, 2022, 5:06 p.m.