crost.decomp

Apply Croston's decomposition on a time series.

 ~croston ~crost ~crost.decomp

Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).

Nikolaos Kourentzes

tsintermittent

Intermittent Time Series Forecasting

Fotios Petropoulos

crost.decomp function

<dl> <dt>data</dt>
<dd>Intermittent demand time series.</dd> <dt>init</dt>
<dd>Initial values for intervals. This can be: 1. x - Numerical value; 2. "naive" - Initial interval is the first interval from start of time series; 3. "mean" - Initial interval is the mean of all in sample intervals.</dd></dl>

Arguments

Author

Croston's decomposition — crost.decomp

<dl>

 <dt>data</dt>
<dd>Intermittent demand time series.</dd>

 <dt>init</dt>
<dd>Initial values for intervals. This can be: 1. x - Numerical value; 2. "naive" - Initial interval is the first interval from start of time series; 3. "mean" - Initial interval is the mean of all in sample intervals.</dd>

</dl>

crost.decomp: Croston's decomposition

Description

Usage

Value

Arguments

Author

See Also

Examples