stlTDNN: STL Decomposition and TDNN Hybrid Time Series Forecasting

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Version:	0.1.0
Depends:	R (≥ 2.10)
Imports:	forecast, nnfor
Published:	2021-02-24
Author:	Girish Kumar Jha [aut, cre], Ronit Jaiswal [aut, ctb], Kapil Choudhary [ctb], Rajeev Ranjan Kumar [ctb]
Maintainer:	Girish Kumar Jha <girish.stat at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	stlTDNN results

Documentation:

Reference manual:

stlTDNN.pdf

Downloads:

Package source:	stlTDNN_0.1.0.tar.gz
Windows binaries:	r-devel: stlTDNN_0.1.0.zip, r-release: stlTDNN_0.1.0.zip, r-oldrel: stlTDNN_0.1.0.zip
macOS binaries:	r-release (arm64): stlTDNN_0.1.0.tgz, r-oldrel (arm64): stlTDNN_0.1.0.tgz, r-release (x86_64): stlTDNN_0.1.0.tgz, r-oldrel (x86_64): stlTDNN_0.1.0.tgz

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