{"id":1675,"date":"2020-10-25T23:34:28","date_gmt":"2020-10-25T23:34:28","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1675"},"modified":"2020-10-27T20:50:17","modified_gmt":"2020-10-27T20:50:17","slug":"isf-2020-using-information-from-the-business-environment-to-improve-forecasting","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2020\/10\/25\/isf-2020-using-information-from-the-business-environment-to-improve-forecasting\/","title":{"rendered":"ISF 2020: A geometry inspired hierarchical forecasting methodology"},"content":{"rendered":"

The slides for my presentation at the International Symposium on Forecasting 2020 are available here.<\/p>\n

Building on the work by Pangiotelis et al. (2020)<\/a> we investigate the implications of the geometric interpretation of hierarchical forecasting further. We propose a new framework for generating hierarchical forecasts, which encompasses previous hierarchical methods while providing insights on their behaviour. A key takeaway is that it is possible to obtain more efficient solutions to the hierarchical forecasting problem. Nonetheless, even though these may be more efficient, the optimisation remains non-trivial. In this work, we identify a series of approximations that balance the efficiency gains with the optimisation complexity to provide superior hierarchical forecasts, as evidenced in our empirical evaluation.<\/p>\n

You can find the slides here<\/a>.<\/p>\n

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