Tag Archives: MAPA

Forecasting with Temporal Hierarchies

This is a talk that I am giving today at the University of Sydney Business School. This research builds upon MAPA and cross-sectional hierarchical forecasting, in particular optimal combinations. Temporal hierarchies reconcile across time, resulting in accurate short and long-term forecasts that can lead to aligned plans and decisions. Temporal hierarchies can be used with… Read More »

ISF 2015 and invited session on “Forecasting with Combinations and Hierarchies”

The International Symposium on Forecasting (ISF 2015) was held this week in Riverside, CA. It was a very interesting conference, with stimulating talks and a wide variety of forecasting related topics, both for academics and practitioners. It is a highly recommended conference to attend. I organised the invited session on the topic of “Forecasting with… Read More »

Experimenting with Shiny for R

Shiny is a web application framework for R. The idea is simple: deploy R code in webpages. This might prove useful when user interaction is required, for instance to design and deploy forecasting experiments that need human participants. I gave it a try to see how easy is it to build a demo. Assuming your… Read More »

MAPA and intermittent demand forecasting

Recently I posted about a paper I co-authored with Fotios Petropoulos, now in JORS: Forecast Combinations for Intermittent Demand. There we found that for intermittent demand data using multiple levels of temporal aggregation, forecasting them with the appropriate models and finally combining the forecasts performed best. This approach has many analogies with the MAPA algorithm… Read More »

Improving your forecasts using 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… Read More »