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 »
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 »
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 »
Last February Facebook open sourced its Prophet forecasting tool. Since, it had appeared in quite a few discussions online. A good thing about Prophet is that it one can use it very easily through R (and Python). This gave me the opportunity to benchmark it against some more standard – and not! – forecasting models… Read More »
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 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 »
You can find the material from yesterday’s workshop at the International Symposium on Forecasting, 2017 here. The workshop notes assume some knowledge of what the various forecasting methods do, and the main focus is on showing which functions to use and how, so as to perform a wide variety of forecasting tasks: Time series exploration… Read More »
The effects of temporal aggregation In this post I will demonstrate the effects of temporal aggregation and motivate the use of multiple temporal aggregation (MTA). I will not delve into the econometric aspects of the discussion, but it is worthwhile to summarise key findings from the literature. A concise forecasting related summary is available in… Read More »
Over the last years I have been working (with my co-authors!) on the idea of Multiple Temporal Aggregation (MTA) for time series forecasting. A number of papers have been published introducing and developing the idea further, or testing its effectiveness for forecasting. In this series of blog posts I will try to summarise the progress… Read More »