Improving forecasting via multiple temporal aggregation

In most business forecasting applications, the problem usually directs the sampling frequency of the data that we collect and use for forecasting. Conventional approaches try to extract information from the historical observations to build a forecasting model. In this article, we explore how transforming the data through temporal aggregation allows us to gather additional information about the series at hand, resulting in better forecasts. We discuss a newly introduced modelling approach that combines information from many different levels of temporal aggregation, augmenting various features of the series in the process, into a robust and accurate forecast.