14.1 Multivariate Time Series
So far, this book has focused on forecasting individual demand time series, one at a time. Limiting our discussion in this way was useful to create focus; however, in most practical situations, forecasters may have to deal with thousands of time series at once. Further, these time series are related to each other, either because products are substitutes or complements or because hierarchical levels overlap. Further, forecasters have an interest in many different hierarchical levels. Consider, for example, that several time series may represent a portfolio of products; products are grouped into categories, but also in turn are divided into multiple stock-keeping units (SKUs) as ...
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