Elucidate structure in intermittent demand time series
Nikolaos Kourentzes and George Athanasopoulos, 2020. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.05.046
Tag Archives: temporal aggregation
Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Kourentzes and Vassilios Assimakopoulos, 2020. Applied Energy. https://doi.org/10.1016/j.apenergy.2019.114339
Forecasting keynote at AMLC 2019
A few weeks ago I gave a talk at Amazon’s 2019 AMLC in Seattle. The talk was focused on current research in temporal and cross-temporal hierarchies. People who have been following my blog will be familiar with the topic and recent advances. This talk is different in the sense that it does not go in… Read More: Forecasting keynote at AMLC 2019 »
Towards the “one-number forecast”
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: Towards the “one-number forecast” »
Cross-temporal coherent forecasts for Australian tourism
Nikolaos Kourentzes and George Athanasopoulos, 2019, Annals of Tourism Research. https://doi.org/10.1016/j.annals.2019.02.001
MAPAx example for R
I have been asked a few time to provide an example how to use MAPAx, from the MAPA package for R, so I prepared this blog post. I admit the documentation could be better, so I put together this example from a retailing case – the original setting MAPAx was developed for (see paper here).… Read More: MAPAx example for R »
Multiple temporal aggregation: the story so far. Part IV: Temporal Hierarchies
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 temporal aggregation: the story so far. Part IV: Temporal… »
ISF2017 presentation: Call centre forecasting using temporal aggregation
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: ISF2017 presentation: Call centre forecasting using temporal aggregation »
Multiple temporal aggregation: the story so far. Part III: MAPA
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: Multiple temporal aggregation: the story so far. Part III: MAPA »
International Journal of Forecasting 2014-2015 best paper award
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: International Journal of Forecasting 2014-2015 best paper award »