Most data sets used by economists are collected with after-the-fact surveys and the time aggregation is done by the survey respondents who produce, for example, monthly aggregates not actual transactions. 21st century digital transaction technologies will increasingly allow the collection of actual transactions, which will create an important new set of opportunities for forming time aggregates. This paper uses a transaction-by-transaction data set on purchases of diesel fuel by over-the-road truckers to form amonthly diesel volume index from 1999 to 2012 purged of weekday, holiday and calendar effects. These high-frequency data allow new and more accurate ways to correct for (1) the variability in the weekday composition of months and (2) the drift of holiday effects between months. These corrections have substantial effects on month-to-month comparisons.