Together with Devon Barrow and Sven Crone, we gave a talk at the recent OR 62 conference, moderated by Christina Phillips. The topic was: “The quest for greater forecasting accuracy: Perspectives from Statistics & Machine Learning”. I have worked with both Devon and Sven in the past years and the three of us share quite… Read More »
Last week we run the first workshop of the Forecasting Forum Scandinavia, hoping to start an ongoing discussion between academia and practice around forecasting and predictive analytics. The vision is for this to be the catalyst in: providing innovative solutions to real business problems, at a rigorous scientific standard; shorten the path to implementing innovative… Read More »
1. Introductory remarks One of the recurrent topics in online discussions on sales forecasting and demand planning is the idea of the “one-number forecast”, that is a common view of the future on which multiple plans and decisions can be made, from different functions of an organisation. In principle, this is yet another idea around… Read More »
Nikolaos Kourentzes and Yves Sagaert, Foresight: The International Journal of Applied Forecasting, 2018, Issue 48. This is a modified version of the paper that appears in Foresight issue 48. This provides a simplified version of the modelling methodology described in this paper and applied here and here. Introduction Using leading indicators for business forecasting has… Read More »
I recently got my hands on a physical copy of my new book: Principles of Business Forecasting (2nd edition). Ord, K., Fildes, R. and Kourentzes, N., 2017. Principles of business forecasting. 2nd ed. Wessex Press Publishing Co. I was invited by Keith Ord and Robert Fildes to join them in writing the much-revised 2nd edition… Read More »
Temporal Hierarchies In the previous post we saw how the Multiple Aggregation Prediction Algortihm (MAPA) implements the ideas of MTA. We also saw that it has some limitations, particularly requiring splitting forecasts into subcomponents (level, trend and seasonality). Although some forecasting methods provide such outputs naturally, for example Exponential Smoothing and Theta, others do not.… Read More »
Multiple Aggregation Prediction Algorithm (MAPA) In this third post about modelling with Multiple Temporal Aggregation (MTA), I will explain how the Multiple Aggregation Prediction Algorithm (MAPA) works, which was the first incarnation of MTA for forecasting. MAPA is quite simple in its logic: a time series is temporally aggregated into multiple levels, at each level… Read More »
You can find the material from yesterday’s workshop at the International Symposium on Forecasting, 2017 here. The workshop notes assume some knowledge of what the various forecasting methods do, and the main focus is on showing which functions to use and how, so as to perform a wide variety of forecasting tasks: Time series exploration… Read More »
The effects of temporal aggregation In this post I will demonstrate the effects of temporal aggregation and motivate the use of multiple temporal aggregation (MTA). I will not delve into the econometric aspects of the discussion, but it is worthwhile to summarise key findings from the literature. A concise forecasting related summary is available in… Read More »
Over the last years I have been working (with my co-authors!) on the idea of Multiple Temporal Aggregation (MTA) for time series forecasting. A number of papers have been published introducing and developing the idea further, or testing its effectiveness for forecasting. In this series of blog posts I will try to summarise the progress… Read More »