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The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.
| Version: | 0.1.0 |
| Imports: | forecast, stats |
| Published: | 2024-05-10 |
| Author: | Saikath Das [aut, cre], Bishal Gurung [aut], Achal Lama [aut], KN Singh [aut] |
| Maintainer: | Saikath Das <saikathdas007 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| In views: | TimeSeries |
| CRAN checks: | EXPAR results |
| Reference manual: | EXPAR.pdf |
| Package source: | EXPAR_0.1.0.tar.gz |
| Windows binaries: | r-devel: EXPAR_0.1.0.zip, r-release: EXPAR_0.1.0.zip, r-oldrel: EXPAR_0.1.0.zip |
| macOS binaries: | r-release (arm64): EXPAR_0.1.0.tgz, r-oldrel (arm64): EXPAR_0.1.0.tgz, r-release (x86_64): EXPAR_0.1.0.tgz, r-oldrel (x86_64): EXPAR_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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