decompDL: Decomposition Based Deep Learning Models for Time Series Forecasting

Hybrid model is the most promising forecasting method by combining decomposition and deep learning techniques to improve the accuracy of time series forecasting. Each decomposition technique decomposes a time series into a set of intrinsic mode functions (IMFs), and the obtained IMFs are modelled and forecasted separately using the deep learning models. Finally, the forecasts of all IMFs are combined to provide an ensemble output for the time series. The prediction ability of the developed models are calculated using international monthly price series of maize in terms of evaluation criteria like root mean squared error, mean absolute percentage error and, mean absolute error. For method details see Choudhary, K. et al. (2023). <https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: keras, tensorflow, reticulate, tsutils, stats, BiocGenerics, utils, graphics, magrittr, Rlibeemd, TSdeeplearning, VMDecomp
Published: 2023-12-04
Author: Kapil Choudhary [aut, cre], Girish Kumar Jha [aut, ths, ctb], Ronit Jaiswal [ctb], Rajeev Ranjan Kumar [ctb]
Maintainer: Kapil Choudhary <kapiliasri at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: decompDL results

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Reference manual: decompDL.pdf

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Package source: decompDL_0.1.0.tar.gz
Windows binaries: r-prerel: decompDL_0.1.0.zip, r-release: decompDL_0.1.0.zip, r-oldrel: decompDL_0.1.0.zip
macOS binaries: r-prerel (arm64): decompDL_0.1.0.tgz, r-release (arm64): decompDL_0.1.0.tgz, r-oldrel (arm64): decompDL_0.1.0.tgz, r-prerel (x86_64): decompDL_0.1.0.tgz, r-release (x86_64): decompDL_0.1.0.tgz

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