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decompDL  

Decomposition Based Deep Learning Models for Time Series Forecasting
View on CRAN: Click here


Download and install decompDL package within the R console
Install from CRAN:
install.packages("decompDL")

Install from Github:
library("remotes")
install_github("cran/decompDL")

Install by package version:
library("remotes")
install_version("decompDL", "0.1.0")



Attach the package and use:
library("decompDL")
Maintained by
Kapil Choudhary
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-12-04
Latest Update: 2023-12-04
Description:
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). .
How to cite:
Kapil Choudhary (2023). decompDL: Decomposition Based Deep Learning Models for Time Series Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/decompDL. Accessed 22 Dec. 2024.
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Complete documentation for decompDL
Functions, R codes and Examples using the decompDL R package
Some associated functions: Data_Maize . ceemdGRU . ceemdLSTM . ceemdRNN . eemdGRU . eemdLSTM . eemdRNN . emdGRU . emdLSTM . emdRNN . vmdGRU . vmdLSTM . vmdRNN . 
Some associated R codes: ceemdGRU.R . ceemdLSTM.R . ceemdRNN.R . eemdGRU.R . eemdLSTM.R . eemdRNN.R . emdGRU.R . emdLSTM.R . emdRNN.R . vmdGRU.R . vmdLSTM.R . vmdRNN.R .  Full decompDL package functions and examples
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