# croston

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

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

- Keywords
- ts

##### Usage

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

##### Arguments

- y
a numeric vector or time series of class

`ts`

- h
Number of periods for forecasting.

- alpha
Value of alpha. Default value is 0.1.

- 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. 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:

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`

.

The name of the forecasting method as a character string

Point forecasts as a time series

The original time series (either `object`

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

).

Residuals from the fitted model. That is y minus fitted values.

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

```
# NOT RUN {
y <- rpois(20,lambda=.3)
fcast <- croston(y)
plot(fcast)
# }
```

*Documentation reproduced from package forecast, version 8.1, License: GPL (>= 3)*