farfima: Order Selection and Forecasting in Long-Memory Functional Time
Series
Provides tools for modelling, forecasting and visualising
long-memory functional time series with the functional autoregressive
fractionally integrated moving-average model. The model is fitted in three
steps, each of which can also be used on its own. First, the memory
parameter is estimated from the leading principal component scores, for
example by local Whittle or by bias-corrected local polynomial Whittle with
noise. Second, the autoregressive and moving-average orders of the
fractionally differenced curves are selected by a functional information
criterion, functional Box-Jenkins or cross-validation. Third, the operators
are estimated. The point forecasts come with conformal prediction bands that
quantify their uncertainty. Visualisation tools include improved rainbow
plots and plots of the fitted model and its forecasts.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
FCVAR, far, ftsa, longmemo, rainbow, sde, stats, utils, graphics, grDevices, vars, withr, wwntests |
| Suggests: |
demography, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-11-10 |
| DOI: |
10.32614/CRAN.package.farfima (may not be active yet) |
| Author: |
Chang Liu [aut, cre],
Han Lin Shang [aut],
Garth Tarr [aut],
Samuel Müller [aut] |
| Maintainer: |
Chang Liu <chang.liu45 at students.mq.edu.au> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
farfima results |
Documentation:
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