# croston: Forecasts for intermittent demand using Croston's method In robjhyndman/forecast: Forecasting Functions for Time Series and Linear Models

## Description

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

## Usage

 `1` ```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.

## 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.

Rob J Hyndman

## 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.

`ses`.
 ```1 2 3``` ```y <- rpois(20,lambda=.3) fcast <- croston(y) plot(fcast) ```