Category Archives: Blog

Can you predict the closing price of Bitcoin?

Lately, there has been a lot of talks whether Bitcoin is a bubble (about to burst) or not. The discussion is quite interesting, not only because there is potentially a lot of money involved, but also because it shows how our economic theories are primarily unclear and secondarily incomplete on concepts such as bubbles and… Read More »

nnfor on github

I have put up a github repository for the nnfor package for R: 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: Related PostsForecasting time series with neural networks in R Can neural networks predict… Read More »

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 »

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 »

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 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 »