idclass: Time series categorisation for intermittent demand In tsintermittent: Intermittent Time Series Forecasting

 idclass R Documentation

Time series categorisation for intermittent demand

Description

Time series categorisation for intermittent demand data.

Usage

```idclass(data,type=c("PKa","SBC","KHa","KH","PK"),a.in=NULL,
outplot=c("summary","detail","none"),plot.focus=NULL)
```

Arguments

 `data` Time series dataset. Each column is a series. Alternatively this can be a single series. `type` Type of categorisation: 1. "SBC" - Syntetos Boylan Croston; 2. "KH" - Kostenko Hyndman (exact*); 3. "KHa" - Kostenko Hyndman (approximate); 4. "PK" - Petropoulos Kourentzes (exact*); 5. "PKa" - Petropoulos Kourentzes (approximate). *These are computationally expensive, as SBA is optimised for each time series. `a.in` Vector of SBA demand interval smoothing parameters. This must be same length as number of series. This is used for categorisations "KH" and "PK". If a.in == NULL then the parameters are calculated internally using MAR as a cost function. `outplot` Plot results of categorisation: 1. "summary" - simlified plot that reports number of series in each class and cut-off points; 2. "detail" - scatterplot between average interdemand interval (p) and squared coefficient of variation of non-zero demand (CV^2). Series that are categorised for SBA or SES are plotted in shaded areas; 3. "none" - do not produce plot. `plot.focus` Only relevant to outplot == "detail". Can be used to specify the maximum p and CV^2 to plot, so that the scatterplot can be focused on the separation area between the categories. Use vector of two elements. First one is max p and second one is max CV^2. Example: plot.focus=c(1.5,1.5). If NULL then maximums are defined from the dataset.

Value

 `idx.croston` Index of series that are categorised under Croston. `idx.sba` Index of series that are categorised under SBA. `idx.ses` Index of series that are categorised under SES. Provided only for "PK" and "PKa" types. `cv2` Coefficient of variation squared of non-zero demand. `p` Inter-demand interval. `summary` Summary of number of series under each category.

Author(s)

Nikolaos Kourentzes

References

Classification schemes described in: F. Petropoulos and N. Kourentzes, 2015, Journal of Operational Research Society. https://link.springer.com/article/10.1057/jors.2014.62. https://kourentzes.com/forecasting/2014/05/13/forecast-combinations-for-intermittent-demand/

Optimisation of the methods described in: N. Kourentzes, 2014, On intermittent demand model optimisation and selection, International Journal of Production Economics, 156: 180-190. doi: 10.1016/j.ijpe.2014.06.007.

`crost`, `tsb`, `imapa`.
```# Create/load some data. Each column is a time series