The function applies several models on the provided time series and identifies what type of demand it is based on an information criterion.
aid(y, ic = c("AICc", "AIC", "BICc", "BIC"), level = 0.99,
loss = "likelihood", ...)Class "aid" is returned, which contains:
y - The original data;
models - All fitted models;
ICs - Values of information criteria;
type - The type of the identified demand;
stockouts - List with start and end ids of potential stockouts;
new - Binary showing whether the data start with the abnormal number of zeroes. Must be a new product then;
obsolete - Binary showing whether the data ends with the abnormal number of zeroes. Must be product that was discontinued (obsolete).
The vector of the data.
Information criterion to use.
The confidence level used in stockouts identification.
The type of loss function to use in model estimation. See alm for possible options.
Other parameters passed to the alm() function.
Ivan Svetunkov, ivan@svetunkov.ru
In the first step, function creates inter-demand intervals and fits a model with LOWESS of it assuming Geometric distribution. The outliers from this model are treated as potential stock outs.
In the second step, the function creates explanatory variables based on LOWESS of the original data, then applies Normal, Normal + Bernoulli models and selects the one that has the lowest IC. Based on that, it decides what type of demand the data corresponds to: regular or intermittent. Finally, if the data is count, the function will identify that.
# Data from Poisson distribution
y <- rpois(120, 0.7)
aid(y)
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