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 `ts`

h

Number of periods for forecasting.

alpha

Value of alpha. Default value is 0.1.

x

Deprecated. Included for backwards compatibility.

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.

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.

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`

.

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