Forecasts for intermittent demand using Croston's method
Returns forecasts and other information for Croston's forecasts applied to y.
croston(y, h = 10, alpha = 0.1, x = y)
y |
a numeric vector or time series of class |
h |
Number of periods for forecasting. |
alpha |
Value of alpha. Default value is 0.1. |
x |
Deprecated. Included for backwards compatibility. |
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.
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 |
residuals |
Residuals from the fitted model. That is y minus fitted values. |
fitted |
Fitted values (one-step forecasts) |
The function summary is used to obtain and print a summary of the
results, while the function plot produces a plot of the forecasts.
The generic accessor functions fitted.values and residuals
extract useful features of the value returned by croston and
associated functions.
Rob J Hyndman
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.
ses.
y <- rpois(20,lambda=.3) fcast <- croston(y) plot(fcast)
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