Improved Prediction Intervals for ARIMA Processes and Structural Time Series R package tsPI version 1.0.4 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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. Software Imports: KFAS Suggests: testthat Jouni Helske <jouni.helske@iki.fi> Comprehensive R Archive Network (CRAN) Jouni Helske GPL-3 2023-09-04 application/tgz https://CRAN.R-project.org/package=tsPI doi:10.32614/CRAN.package.tsPI