forecast.croston_model: Forecasts for intermittent demand using Croston's method

In forecast: Forecasting Functions for Time Series and Linear Models

Forecasts for intermittent demand using Croston's method

Description

Returns forecasts and other information for Croston's forecasts applied to y.

Usage

## S3 method for class 'croston_model'
forecast(object, h = 10, ...)

croston(y, h = 10, alpha = 0.1, type = c("croston", "sba", "sbj"), x = y)

Arguments

object	An object of class croston_model as returned by croston_model().
h	Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).
...	Additional arguments affecting the forecasts produced. If model = NULL, forecast.ts passes these to ets() or stlf() depending on the frequency of the time series. If model is not NULL, the arguments are passed to the relevant modelling function.
y	a numeric vector or univariate time series of class ts
alpha	Value of alpha. Default value is 0.1.
type	Which variant of Croston's method to use. Defaults to "croston" for Croston's method, but can also be set to "sba" for the Syntetos-Boylan approximation, and "sbj" for the Shale-Boylan-Johnston method.
x	Deprecated. Included for backwards compatibility.

Details

Based on Croston's (1972) method for intermittent demand forecasting, also described in Shenstone and Hyndman (2005). Croston's method involves using simple exponential smoothing (SES) on the non-zero elements of the time series and a separate application of SES to the times between non-zero elements of the time series. The smoothing parameters of the two applications of SES are assumed to be equal and are denoted by alpha.

Note that prediction intervals are not computed as Croston's method has no underlying stochastic model.

Value

An object of class forecast.

forecast class

An object of class forecast is a list usually containing at least the following elements:

model

A list containing information about the fitted model

method

The name of the forecasting method as a character string

mean

Point forecasts as a time series

lower

Lower limits for prediction intervals

upper

Upper limits for prediction intervals

level

The confidence values associated with the prediction intervals

x

The original time series.

residuals

Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.

fitted

Fitted values (one-step forecasts)

The function summary can be used to obtain and print a summary of the results, while the functions plot and autoplot produce plots of the forecasts and prediction intervals. The generic accessor functions fitted.values and residuals extract various useful features from the underlying model.

Author(s)

Rob J Hyndman

References

Croston, J. (1972) "Forecasting and stock control for intermittent demands", Operational Research Quarterly, 23(3), 289-303.

Shale, E.A., Boylan, J.E., & Johnston, F.R. (2006). Forecasting for intermittent demand: the estimation of an unbiased average. Journal of the Operational Research Society, 57(5), 588-592.

Shenstone, L., and Hyndman, R.J. (2005) "Stochastic models underlying Croston's method for intermittent demand forecasting". Journal of Forecasting, 24, 389-402.

Syntetos A.A., Boylan J.E. (2001). On the bias of intermittent demand estimates. International Journal of Production Economics, 71, 457–466.

See Also

ses().

Examples

y <- rpois(20, lambda = 0.3)
fcast <- croston(y)
autoplot(fcast)

forecast documentation built on March 18, 2026, 9:07 a.m.