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InterNL
View on CRAN: Click
here
Download and install InterNL package within the R console
Install from CRAN:
install.packages("InterNL")
Install from Github:
library("remotes")
install_github("cran/InterNL")
Install by package version:
library("remotes")
install_version("InterNL", "0.1.0")
Attach the package and use:
library("InterNL")
Maintained by
Dr. Md Yeasin
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-04-18
Latest Update: 2024-04-18
Description:
Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. <doi:10.1016/j.hazadv.2023.100325> and Paul and Yeasin <doi:10.1371/journal.pone.0272999>.
How to cite:
Dr. Md Yeasin (2024). InterNL: Time Series Intervention Model Using Non-Linear Function. R package version 0.1.0, https://cran.r-project.org/web/packages/InterNL
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