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.

Documentation

Manual: tsPI.pdf
Vignette: None available.

Maintainer: Jouni Helske <jouni.helske at jyu.fi>

Author(s): Jouni Helske

Install package and any missing dependencies by running this line in your R console:

install.packages("tsPI")

Depends
Imports KFAS
Suggests testthat
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Package tsPI
Materials
URL
Task Views TimeSeries
Version 1.0.1
Published 2016-03-17
License GPL-3
BugReports https://github.com/helske/tsPI/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks tsPI check results
Package source tsPI_1.0.1.tar.gz