idclass | R Documentation |

Time series categorisation for intermittent demand data.

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

`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. |

`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. |

Nikolaos Kourentzes

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 dataset <- simID(100,60,idi=1.15,cv2=0.3) idclass(dataset)

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