Forecasts for intermittent demand using Croston's method
Returns forecasts and other information for Croston's forecasts applied to x.
croston(x, h=10, alpha=0.1)
- a numeric vector or time series
- Number of periods for forecasting.
- Value of alpha. Default value is 0.1.
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
Note that prediction intervals are not computed as Croston's method has no underlying stochastic model. The separate forecasts for the non-zero demands, and for the times between non-zero demands do have prediction intervals based on ETS(A,N,N) models.
- An object of class
"forecast"is a list containing at least the following elements:
model A list containing information about the fitted model. The first element gives the model used for non-zero demands. The second element gives the model used for times between non-zero demands. Both elements are of class
method The name of the forecasting method as a character string mean Point forecasts as a time series x The original time series (either
objectitself or the time series used to create the model stored as
residuals Residuals from the fitted model. That is x minus fitted values. fitted Fitted values (one-step forecasts)
- The function
summaryis used to obtain and print a summary of the results, while the function
plotproduces a plot of the forecasts.
The generic accessor functions
residualsextract useful features of the value returned by
crostonand associated functions.
Croston, J. (1972) "Forecasting and stock control for intermittent demands", Operational Research Quarterly, 23(3), 289-303.
Shenstone, L., and Hyndman, R.J. (2005) "Stochastic models underlying Croston's method for intermittent demand forecasting". Journal of Forecasting, 24, 389-402.
x <- rpois(20,lambda=.3) fcast <- croston(x) plot(fcast)