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Macquarie University    The University of Sydney

farfima: Order Selection and Forecasting in Long-Memory Functional Time Series

Chang Liu, Han Lin Shang, Garth Tarr and Samuel Müller
Package website »

Overview

farfima provides tools for modelling, forecasting and visualising long-memory functional time series with the functional autoregressive fractionally integrated moving-average (FARFIMA) 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 by the functional Hannan-Rissanen method. 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.

Installation

# install.packages("remotes")
remotes::install_github("chang-liu-0122/farfima", build_vignettes = TRUE)

farfima needs R 4.1.0 or later. Installing it also installs the packages it uses: FCVAR, far, ftsa, longmemo, rainbow, sde, vars, withr and wwntests. The examples below and the vignette also use the demography package.

Getting Started

Curves are stored as a matrix with grid points in rows and time in columns, such as ages by years. fts objects from the rainbow and ftsa packages also work. The example uses French female mortality rates from the demography package, for the years 1901 to 2006, with ages 0 to 94 and a group for ages 95 and above.

library(farfima)
library(demography)

# French female log death rates
fr = extract.ages(extract.years(fr.mort, 1901:2006), 0:95)
x = log10(smooth.demogdata(fr, method = "mspline", b = 65, weight = TRUE)$rate$female)
plot_curves(x)

# Training, validation and test samples
blocks = split_curves(x, train = 0.6, valid = 0.2)

# Fit the model and calibrate the conformal prediction bands
fit = farfima(blocks$train, blocks$valid, h = 10, level = c(0.8, 0.95), trim = 20, seed = 1)
fit
plot(fit, type = "residuals")

# Forecast, and evaluate on the test sample
fc = predict(fit, test = blocks$test)
fc
plot(fc, h = 1, band = "AC", xlab = "Age", ylab = "log10 death rate")

The test evaluation reports the RMSPE and MAPE of the point forecasts, and the ECP, CPD, MIS and WIS of the conformal prediction bands.

Main options of farfima()

Argument Sets Options
d memory parameter "lw", "dfa", "lpwn1", "lpwn2", "bc_lpwn1", "bc_lpwn2" (default), or a known value
order FARMA orders "ic_full" (default), "ic_auto", "bj", "cv", or known orders c(p, q)
p, q candidate orders e.g. p = 0:5, q = 0:5
method model "farma" (default), or "far" for the FAR(1) benchmark
h longest forecast horizon e.g. h = 10
level levels of the conformal prediction bands e.g. c(0.8, 0.95)
band conformal prediction bands "SR", "NS", "AC", "PID", "AcMCP"
window window at each forecast origin "expanding" (default) or "fixed"

Steps of the analysis

The analysis is composed of four steps. farfima() runs steps 1 to 3 together and calibrates the conformal prediction bands, and predict() makes the forecasts in step 4. Steps 1 to 3 can also be run on their own, with the functions below.

Step Function Methods
1. Memory parameter estimate_memory() local Whittle, DFA, LPWN, bias-corrected LPWN
2. FARMA orders select_order() functional information criterion, functional Box-Jenkins, cross-validation
3. Operators estimate_operators() functional Hannan-Rissanen
4. Forecasts predict() point forecasts with conformal prediction bands (SR, NS, AC, PID, AcMCP), evaluated on a test sample

Plots

Code Shows
plot_curves(x) rainbow plot of the curves, or one panel per period
plot(fit) the fitted curves, or the residual curves with type = "residuals"
plot(fit$order_selection) the criterion over the candidate orders
plot(fit$operators) heatmaps of the estimated operators
plot(fc) forecasts with their conformal prediction bands, by curve, horizon, grid point or recent years

Folders and Files

Package files

File Purpose
DESCRIPTION Package name, version, authors, licence and the packages farfima uses
NAMESPACE Functions that farfima exports and imports, generated by roxygen2
README.md This page, which is also the home page of the package website
_pkgdown.yml Layout of the package website
.github/workflows/pkgdown.yaml Instructions for GitHub to rebuild the package website after each push
.Rbuildignore, .gitignore Files left out of the package build and of git
farfima.Rproj RStudio project

R/: source code

Exported functions, one file each:

File Purpose
farfima.R Fit the FARFIMA model and calibrate the conformal prediction bands
predict.farfima.R Point forecasts with conformal prediction bands, and their evaluation on a test sample
split_curves.R Split the curves into training, validation and test samples
plot_curves.R Rainbow plots of the curves, in one panel or one panel per period
estimate_memory.R Memory parameter estimation
frac_diff.R Fractional differencing or integration of functional time series
select_order.R FARMA order selection
estimate_operators.R FARMA operator estimation
sim_farfima.R Simulation of FARFIMA(p, d, q) curves

Methods for fits and forecasts, including visualisation:

File Purpose
fitted.farfima.R, residuals.farfima.R, coef.farfima.R Fitted curves, residual curves and estimates of a fit
plot.farfima.R Visualisation of the fitted and residual curves
plot.farfima_forecast.R Visualisation of the forecasts and conformal prediction bands
plot.farfima_order.R, plot.farfima_operators.R Visualisation of the order selection criterion, and heatmaps of the operators
as.data.frame.farfima_forecast.R Forecasts and conformal prediction bands as a data frame
print.*.R, summary.*.R Printed summaries of fits, forecasts and estimates

Internal functions:

File Purpose
lw.R, dfa.R, lpwn.R Local Whittle, DFA and LPWN estimators
sieve_bootstrap.R Sieve bootstrap for the bias-corrected LPWN estimators
covariance.R Covariance-type operators and the leading principal component
order_selection.R, ic_internal.R Functional information criterion, functional Box-Jenkins and cross-validation
farma.R Functional Hannan-Rissanen estimation and the stability check
forecast_paths.R, origin.R Forecasts on the original scale at each forecast origin
calibrate.R, conformal.R Calibration of the SR, NS, AC, PID and AcMCP conformal prediction bands
simulate_kernels.R Brownian motion innovations and operator kernels for sim_farfima()
input.R, labels.R, plot_helpers.R Input checks, printed labels and plotting helpers
farfima-package.R The package help page, ?farfima-package

man/: help pages

The help pages, generated by roxygen2 from the comments in R/. man/figures/ holds the logos on this page.

tests/testthat/: tests

One test file per function, such as test-farfima.R and test-predict.R, and helper-curves.R with the simulated curves shared by the tests. Run them with devtools::test().

vignettes/: tutorial

farfima.Rmd introduces each function in turn, with an example on the French mortality data. It is the “Get started” page of the package website, and vignette("farfima") opens it in R.

Documentation

Authors

Name Role Affiliation Email
Chang Liu Author, maintainer Macquarie University chang.liu45@students.mq.edu.au
Han Lin Shang Author Macquarie University hanlin.shang@mq.edu.au
Garth Tarr Author The University of Sydney garth.tarr@sydney.edu.au
Samuel Müller Author Macquarie University samuel.muller@mq.edu.au

License

GPL-2

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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