Programs for analyzing large-scale time series data. They include functions for automatic specification and estimation of univariate time series, for clustering time series, for multivariate outlier detections, for quantile plotting of many time series, for dynamic factor models and for creating input data for deep learning programs. Examples of using the package can be found in the Wiley book 'Statistical Learning with Big Dependent Data' by Daniel Peña and Ruey S. Tsay (2021). ISBN 9781119417385.
Version: 0.0.4 Depends: R (≥ 3.5.0) Imports: stats, glmnet, corpcor, forecast, gsarima, cluster, fGarch, imputeTS, methods, MASS, MTS, TSclust, tsoutliers, Matrix, matrixcalc, rnn Published: 2022-04-27 DOI: 10.32614/CRAN.package.SLBDD Author: Angela Caro [aut], Antonio Elias [aut, cre], Daniel Peña [aut], Ruey S. Tsay [aut] Maintainer: Antonio Elias <antonioefz91 at gmail.com> License: GPL-3 NeedsCompilation: no Materials: NEWS In views: TimeSeries CRAN checks: SLBDD results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=SLBDD to link to this page.
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