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Principles of Business Forecasting 2e

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 »

nnfor on github

I have put up a github repository for the nnfor package for R: https://github.com/trnnick/nnfor I will be putting updates and fixes there, before they are pushed on CRAN. You can also report there bugs. You can install the current github version with:

Congratulations Dr. Sagaert!

Yesterday, Yves Sagaert successfully defended his PhD in a public presentation at Ghent University! Yves’ PhD research has been on: tactical sales forecasting with external leading indicators. It has been a pleasure to work with Yves over the past years! During his PhD he published two papers, with more currently under review: Sagaert, Y.R., Aghezzaf,… Read More »

New R package nnfor: time series forecasting with neural networks

My new R package nnfor is available on CRAN. This collects the various neural network functions that appeared in TStools. See this post for demo of these functions. In summary the package includes: Automatic, semi-automatic or fully manual specification of MLP neural networks for time series modelling, that helps in specifying inputs with lags of the… 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 »