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Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.
| Version: | 1.0.4 |
| Imports: | KFAS |
| Suggests: | testthat |
| Published: | 2023-09-04 |
| DOI: | 10.32614/CRAN.package.tsPI |
| Author: | Jouni Helske |
| Maintainer: | Jouni Helske <jouni.helske at iki.fi> |
| BugReports: | https://github.com/helske/tsPI/issues |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Citation: | tsPI citation info |
| Materials: | ChangeLog |
| In views: | TimeSeries |
| CRAN checks: | tsPI results |
| Reference manual: | tsPI.pdf |
| Package source: | tsPI_1.0.4.tar.gz |
| Windows binaries: | r-devel: tsPI_1.0.4.zip, r-release: tsPI_1.0.4.zip, r-oldrel: tsPI_1.0.4.zip |
| macOS binaries: | r-release (arm64): tsPI_1.0.4.tgz, r-oldrel (arm64): tsPI_1.0.4.tgz, r-release (x86_64): tsPI_1.0.4.tgz, r-oldrel (x86_64): tsPI_1.0.4.tgz |
| Old sources: | tsPI archive |
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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.