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

`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.

## 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 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.

## 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.

## 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.

## Examples

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