Featured Article

Can you spot trend in time series?

Past experiments have demonstrated that humans (with or without formal training) are quite good at visually identifying the structure of time series. Trend is a key component, and arguably the most relevant to practice, as many of the forecasts that affect our lives have to do with potential increases or decreases of economic variables. Forecasters… Read More »

OR59 Keynote: Uncertainty in predictive modelling

I recently presented at the OR59 conference my views and current work (with colleagues) on uncertainty in predictive modelling. I think this is a topic that deserves quite a bit of research attention, as it has substnatial implications for estimation, model selection and eventually decision making. The talk has three parts: Argue (as others before… Read More »

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 »

ISF 2017 presentation: A hierarchical approach to forecasting Scandinavian unemployment

This is joint work with Rickard Sandberg and looks at the implicit connections enforced by hierarchical time series forecasting, between the nodes of the hierarchy, contrasting them to VAR models that captures connections explicitly. Abstract The four major Scandinavian economies (Denmark, Finland, Sweden and Norway) have high workforce mobility and depending on market dynamics the… Read More »

ISF2017 presentation: DIY forecasting – judgement, models & judgmental model selection

This is joint work with Fotios Petropoulos and Kostantinos Nikolopoulos and discusses the performance of experts selecting forecasting models, against automatic statistical model selection, as well as providing guidelines how to maximise the benefits. This is very exciting research, demonstrating the both some limitations of statistical model selection (and avenues for new research), as well… Read More »

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 »

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 »

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 »