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The package fable.intermittent implements probabilistic
methods for intermittent time series in the tidyverts framework. The
methods follow the fable/fabletools
syntax. To fit the methods, use them as arguments of the
model() function. To generate forecasts, pass the fitted
models to the forecast() function, following the tidyr pipeline.
The forecasting methods are the following:
| Method | Description |
|---|---|
BETANBB() |
Bayesian dynamic negative binomial model with a beta prior on the probability parameter. |
EMPDISTR() |
Empirical resampling baseline that forecasts from the observed distribution. |
GAMPOISB() |
Bayesian dynamic Poisson model with a gamma prior on the rate parameter. |
HSPES() |
Exponential smoothing model with a hurdle-shifted Poisson forecast distribution. |
MARWAL() |
ARMA model with a Markov walk on the occurrence and Gaussian forecast distribution. |
NEGBINES() |
Exponential smoothing model with a negative binomial forecast distribution. |
STATICDISTR() |
Static count-distribution model that selects among candidate distributions by AIC or BIC. |
TWEES() |
Exponential smoothing model with a Tweedie forecast distribution. |
VZ() |
Bootstrap method based on Croston decomposition sampling demand sizes and intervals. |
WSS() |
Bootstrap method with sampled demand sizes and a Markov-chain for the occurrence. |
The probabilistic forecasts produced by the implemented methods are
distributional
objects. Among the predictive distribution used by the methods, there is
the Tweedie distribution, for which fable.intermittent
provides a novel implementation. It can be used in the following
ways:
stats package syntax:
dtweedie(), ptweedie(),
qtweedie(), and rtweedie().distributional object
dist_tweedie() and all its methods, such as
density(), CDF(), quantile(),
generate(), and others.Finally, the package releases two data sets in the tsibble format:
auto and raf.
:boom: [2026-TO-DO] fable.intermittent v0.1.0: first release.
You can install the stable version from CRAN:
install.packages("fable.intermittent")You can install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("StefanoDamato/fable.intermittent", build_vignettes = TRUE, dependencies = TRUE)The package follows the standard fable workflow:
tsibble.model().forecast().We provide in this vignette a simple usage example; refer to the package documentation for more details on the methods.
 Stefano Damato (Maintainer) |
 Lorenzo Zambon |
 Dario Azzimonti |
If you encounter a bug, please file a minimal reproducible example on GitHub.