I’m back in California for the next couple of weeks, and will give the following talk at Stanford and UC-Davis. Optimal forecast reconciliation for big time series data Time series can often be naturally disaggregated in a hierarchical or grouped structure. For example, a manufacturing company can disaggregate total demand for their products by country of sale, retail outlet, product type, package size, and so on. As a result, there can be millions of individual time series to forecast at the most disaggregated level, plus additional series to forecast at higher levels of …