Tag Archives: forecast combination

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 »

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

I am organising a special session on the upcoming International Symposium on Forecasting with the topic “Forecasting with Combinations and Hierarchies”. Please contact me if you are interested to contribute to this session. In many applications there are time series that can be hierarchically organised and can be grouped or aggregated in several ways, based… Read More »

Guest post: On the robustness of bagging exponential smoothing

This is a guest blog entry by Fotios Petropoulos. A few months ago, Bergmeir, Hyndman and Benitez made available a very interesting working paper titled “Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation”. In short, they successfully employed the bootstrap aggregation technique for improving the performance of exponential smoothing. The bootstrap technique is… Read More »

Ensemble size and combination operators

Combining forecasts has been shown in many cases to lead to improvements in forecasting performance, in terms of accuracy and bias. This is also common in forecasting with neural networks or other computationally intensive methods, where ensemble forecasts are considered more accurate than individual model forecasts. A useful feature of forecast combination is that it… Read More »