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ARIMAANN
View on CRAN: Click
here
Download and install ARIMAANN package within the R console
Install from CRAN:
install.packages("ARIMAANN")
Install from Github:
library("remotes")
install_github("cran/ARIMAANN")
Install by package version:
library("remotes")
install_version("ARIMAANN", "0.1.0")
Attach the package and use:
library("ARIMAANN")
Maintained by
Mrinmoy Ray
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.ARIMAANN . ARIMAANN results . ARIMAANN.pdf . ARIMAANN_0.1.0.tar.gz . ARIMAANN_0.1.0.zip . ARIMAANN_0.1.0.zip . ARIMAANN_0.1.0.zip . ARIMAANN_0.1.0.tgz . ARIMAANN_0.1.0.tgz . ARIMAANN_0.1.0.tgz . ARIMAANN_0.1.0.tgz . ARIMAANN_0.1.0.tgz . ARIMAANN_0.1.0.tgz . https://CRAN.R-project.org/package=ARIMAANN .
First Published: 2022-10-13
Latest Update: 2022-10-13
Description:
Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) .
How to cite:
Mrinmoy Ray (2022). ARIMAANN: Time Series Forecasting using ARIMA-ANN Hybrid Model. R package version 0.1.0, https://cran.r-project.org/web/packages/ARIMAANN. Accessed 29 Mar. 2025.
Previous versions and publish date:
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Complete documentation for ARIMAANN
Functions, R codes and Examples using
the ARIMAANN R package
Some associated functions: ARIMAANN .
Some associated R codes: ARIMAANN.R . Full ARIMAANN package functions and examples
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