tsPI
1.0.4Improved Prediction Intervals for ARIMA Processes and Structural Time Series
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 60%
- Documented parameters
- 100%
- Return-value docs
- 88%
- References docs
- 22%
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Code & Tests
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5 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.0.4Latest
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 1.0.32019-12-05 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.0.22017-08-07 · diff ↗
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- 1.0.12016-03-17 · diff ↗
- 1.0.02015-11-09
Show 1 earlier events
- RR 3.2.0 released · 2015-04-16
Package metadata
- First published
- 2015-11-09
- Total releases
- 5 / 11 yrs
- License
- GPL-3 OSI
- Download size
- 18 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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