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.
- Version0.1.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release12/04/2023
Team
Kapil Choudhary
Girish Kumar Jha
Show author detailsRolesAuthor, Thesis advisor, ContributorRajeev Ranjan Kumar
Show author detailsRolesContributorRonit Jaiswal
Show author detailsRolesContributor
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- Imports8 packages