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:

modelA 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`

.

methodThe name of the
forecasting method as a character string

meanPoint forecasts as a
time series

xThe original time series (either `object`

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

).

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

fittedFitted 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. 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.

## 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

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