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 »
This is joint work with Devon K. Barrow and Bahman Rostami-Tabar and is an initial exploration of the benefits of using Multiple Temporal Aggregation, as implemented in MAPA for call centre forecasting. The preliminary results are encouraging. More details in the attached presentation. Abstract With thousands of call centres worldwide employing millions and serving billions… 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 »
In the very enjoyable and stimulating International Symposium on Forecasting that just finished in Cairns, Australia, the International Journal of Forecasting (IJF) best paper award for the years 2014-2015 (list of past papers can be found here) was given to one of my papers: Improving forecasting by estimating time series structural components across multiple frequencies!… Read More »
N. Kourentzes, B. Rostami-Tabar and D.K. Barrow, 2017, Journal of Business Research. http://doi.org/10.1016/j.jbusres.2017.04.016
G. Athanasopoulos, R. J. Hyndman, N. Kourentzes and F. Petropoulos, 2017, European Journal of Operational Research. http://doi.org/10.1016/j.ejor.2017.02.046
On 16/11/2016 I gave a talk at the Stockholm School of Economics on the topic of advances in modelling and demand forecasting. Given the diversity of the audience I avoided going into the details of the mathematical formulations, some of which can be found in the appendix of the presentation. The presentation summarises three different… Read More »
The previous version of the MAPA package implemented only the univariate aspect of the algorithm. Version 2.0.1 implements MAPAx as well, which allows incorporating regressors in your forecasts. In this paper we demonstrated the usefulness of temporal aggregation in the case of forecasting demand in the presence of promotions. In particular, we showed that MAPAx… Read More »
Here is the link: https://github.com/trnnick/mapa It has been a long time I wanted to rework the MAPA package for R, but I could not find the time. Finally I got around starting it. There are three objectives in this: Clean up code and introduce S3methods. MAPA was the first package that I wrote! Incorporate the… Read More »
Last August I attended the International Symposium on Inventories, organised by the International Society for Inventory Research (ISIR 2016). This is the second time I had attended this conference and it has been again a very good experience. Interesting talks on a variety of relevant topics to forecasting research. I organised a special track on… Read More »