Package: tsPI 1.0.4
tsPI: Improved Prediction Intervals for ARIMA Processes and Structural Time Series
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.
Authors:
tsPI_1.0.4.tar.gz
tsPI_1.0.4.zip(r-4.5)tsPI_1.0.4.zip(r-4.4)tsPI_1.0.4.zip(r-4.3)
tsPI_1.0.4.tgz(r-4.4-x86_64)tsPI_1.0.4.tgz(r-4.4-arm64)tsPI_1.0.4.tgz(r-4.3-x86_64)tsPI_1.0.4.tgz(r-4.3-arm64)
tsPI_1.0.4.tar.gz(r-4.5-noble)tsPI_1.0.4.tar.gz(r-4.4-noble)
tsPI_1.0.4.tgz(r-4.4-emscripten)tsPI_1.0.4.tgz(r-4.3-emscripten)
tsPI.pdf |tsPI.html✨
tsPI/json (API)
| # Install 'tsPI' in R: |
| install.packages('tsPI', repos = c('https://helske.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/helske/tspi/issues
Last updated 1 years agofrom:f2c774850a. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 15 2024 |
| R-4.5-win-x86_64 | OK | Sep 15 2024 |
| R-4.5-linux-x86_64 | OK | Sep 15 2024 |
| R-4.4-win-x86_64 | OK | Sep 15 2024 |
| R-4.4-mac-x86_64 | OK | Sep 15 2024 |
| R-4.4-mac-aarch64 | OK | Sep 15 2024 |
| R-4.3-win-x86_64 | OK | Sep 15 2024 |
| R-4.3-mac-x86_64 | OK | Sep 15 2024 |
| R-4.3-mac-aarch64 | OK | Sep 15 2024 |
Exports:acv_armaapprox_joint_jeffreysapprox_marginal_jeffreysarima_piavg_coverage_arimaavg_coverage_structdacv_armaexact_joint_jeffreysexact_marginal_jeffreysinformation_armastruct_pi
Dependencies:KFAS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute a theoretical autocovariance function of ARMA process | acv_arma |
| Prediction Intervals for ARIMA Processes with Exogenous Variables Using Importance Sampling | arima_pi |
| Compute the average coverage of the prediction intervals computed by naive plug-in method and 'arima_pi' | avg_coverage_arima |
| Compute the average coverage of the prediction intervals computed by 'struct_pi' and plug-in method | avg_coverage_struct |
| Compute the partial derivatives of theoretical autocovariance function of ARMA process | dacv_arma |
| Large Sample Approximation of Information Matrix for ARMA process | information_arma |
| Compute different types of importance weights based on Jeffreys's prior | approx_joint_jeffreys approx_marginal_jeffreys exact_joint_jeffreys exact_marginal_jeffreys jeffreys |
| Prediction Intervals for Structural Time Series with Exogenous Variables Using Importance Sampling | struct_pi |
| Improved Prediction Intervals for ARIMA Processes and Structural Time Series | tsPI |
