Package: fable.intermittent 0.1.0

fable.intermittent: Forecasting Models for Intermittent Time Series

Extends the 'fable' framework to support forecasting methods specifically designed for intermittent time series data, where demand occurs sporadically with many zero values. All methods produce probabilistic forecasts returned as 'distributional' objects. The returned forecasts can be used to evaluate accuracy, plot and print the results seamlessly with 'fable'. The methods include: Harvey, Fernandes (1989) <doi:10.1080/07350015.1989.10509750>, Willemain, Smart, Schwarz (2004) <doi:10.1016/S0169-2070(03)00013-X>, Zhou, Viswanathan (2011) <doi:10.1016/j.ijpe.2010.09.021>, Snyder, Ord, Beaumont (2012) <doi:10.1016/j.ijforecast.2011.03.009>, Kolassa (2016) <doi:10.1016/j.ijforecast.2015.12.004>, Hasni, Aguir, Babai, Jemai (2019) <doi:10.1080/00207543.2018.1424375>, Damato, Azzimonti, Corani (2025) <doi:10.1016/j.ijforecast.2025.10.001>, Sbrana (2025) <doi:10.1080/01605682.2025.2569661>.

Authors:Stefano Damato [aut, cre, cph], Lorenzo Zambon [aut], Dario Azzimonti [aut]

# Install 'fable.intermittent' in R:

install.packages('fable.intermittent', repos = c('https://stefanodamato.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stefanodamato/fable.intermittent/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

auto - Automotive Spare Parts Demand Dataset
raf - RAF Spare Parts Demand Dataset

On CRAN:

openblas cpp

4.90 score 1 stars 3 scripts 1 mentions 15 exports 44 dependencies

Last updated from:34e7b1c33c. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	266
linux-devel-x86_64	OK	233
source / vignettes	OK	254
linux-release-arm64	OK	235
linux-release-x86_64	OK	221
macos-release-arm64	OK	137
macos-release-x86_64	OK	443
macos-oldrel-arm64	OK	143
macos-oldrel-x86_64	OK	362
windows-devel	OK	266
windows-release	OK	231
windows-oldrel	OK	261
wasm-release	OK	141

Exports:BETANBB dist_tweedie dtweedie EMPDISTR GAMPOISB HSPES MARWAL NEGBINES ptweedie qtweedie rtweedie STATICDISTR TWEES VZ WSS

Dependencies:anytime BH cli cpp11 digest distributional dplyr fabletools farver generics ggdist ggplot2 glue gtable isoband labeling lifecycle lubridate magrittr nloptr numDeriv pillar pkgconfig progressr purrr quadprog R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales stringi stringr tibble tidyr tidyselect timechange tsibble utf8 vctrs viridisLite withr

Forecasting intermittent time series with fable.intermittent

Rendered fromfable.intermittent.Rmdusingknitr::rmarkdownon Jun 19 2026.

Last update: 2026-06-02
Started: 2026-05-29

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Automotive Spare Parts Demand Dataset	auto
Beta-Negative Binomial Bayesian Dynamic Model	BETANBB
Tweedie Distribution	dist_tweedie
Empirical Distribution Resampling	EMPDISTR
Extract fitted values from a BETANBB model	fitted.BETANBB
Extract fitted values from an EMPDISTR model	fitted.EMPDISTR
Extract fitted values from a GAMPOISB model	fitted.GAMPOISB
Extract fitted values from a HSPES model	fitted.HSPES
Extract fitted values from a MARWAL model	fitted.MARWAL
Extract fitted values from a NEGBINES model	fitted.NEGBINES
Extract fitted values from a STATICDISTR model	fitted.STATICDISTR
Extract fitted values from a TWEES model	fitted.TWEES
Extract fitted values from a VZ model	fitted.VZ
Extract fitted values from a WSS model	fitted.WSS
Forecast a BETANBB model	forecast.BETANBB
Forecast an EMPDISTR model	forecast.EMPDISTR
Forecast a GAMPOISB model	forecast.GAMPOISB
Forecast a HSPES model	forecast.HSPES
Forecast a MARWAL model	forecast.MARWAL
Forecast a NEGBINES model	forecast.NEGBINES
Forecast a STATICDISTR model	forecast.STATICDISTR
Forecast a TWEES model	forecast.TWEES
Forecast a VZ model	forecast.VZ
Forecast a WSS model	forecast.WSS
Gamma-Poisson Bayesian Dynamic Model	GAMPOISB
Generate sample paths from a BETANBB model	generate.BETANBB
Generate sample paths from an EMPDISTR model	generate.EMPDISTR
Generate sample paths from a GAMPOISB model	generate.GAMPOISB
Generate sample paths from a HSPES model	generate.HSPES
Generate sample paths from a MARWAL model	generate.MARWAL
Generate sample paths from a NEGBINES model	generate.NEGBINES
Generate sample paths from a STATICDISTR model	generate.STATICDISTR
Generate sample paths from a TWEES model	generate.TWEES
Generate sample paths from a VZ model	generate.VZ
Generate sample paths from a WSS model	generate.WSS
Hurdle-Shifted Poisson Exponential Smoothing	HSPES
Markov Chain Model with Random Walk dynamic	MARWAL
Return model name	model_sum.BETANBB
Return model name	model_sum.EMPDISTR
Return model name	model_sum.GAMPOISB
Return model name	model_sum.HSPES
Return model name	model_sum.MARWAL
Return model name	model_sum.NEGBINES
Return model name	model_sum.STATICDISTR
Return model name	model_sum.TWEES
Return model name	model_sum.VZ
Return model name	model_sum.WSS
Negative Binomial Exponential Smoothing	NEGBINES
RAF Spare Parts Demand Dataset	raf
Extract residuals from a BETANBB model	residuals.BETANBB
Extract residuals from an EMPDISTR model	residuals.EMPDISTR
Extract residuals from a GAMPOISB model	residuals.GAMPOISB
Extract residuals from a HSPES model	residuals.HSPES
Extract residuals from a MARWAL model	residuals.MARWAL
Extract residuals from a NEGBINES model	residuals.NEGBINES
Extract residuals from a STATICDISTR model	residuals.STATICDISTR
Extract residuals from a TWEES model	residuals.TWEES
Extract residuals from a VZ model	residuals.VZ
Extract residuals from a WSS model	residuals.WSS
Static Count Distribution Model	STATICDISTR
Tweedie Distribution Functions	dtweedie ptweedie qtweedie rtweedie tweedie
Tweedie Exponential Smoothing	TWEES
Viswanathan-Zhou Bootstrap Method	VZ
Willemain-Smart-Schwarz Bootstrap Method	WSS