# croston

##### Forecasts for intermittent demand using Croston's method

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

- Keywords
- ts

##### Usage

`croston(x, h=10, alpha=0.1)`

##### Arguments

- x
- a numeric vector or time series
- h
- Number of periods for forecasting.
- alpha
- Value of alpha. Default value is 0.1.

##### 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. 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.

##### Value

- 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 `forecast`

.method The name of the forecasting method as a character string mean Point forecasts as a time series x The original time series (either `object`

itself or the time series used to create the model stored as`object`

).residuals Residuals from the fitted model. That is x 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.

##### References

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.

##### See Also

`ses`

.

##### Examples

```
x <- rpois(20,lambda=.3)
fcast <- croston(x)
plot(fcast)
```

*Documentation reproduced from package forecast, version 5.7, License: GPL (>= 2)*