Y.R. Sagaert, E-H. Aghezzaf, N. Kourentzes and B. Desmet, 2017. European Journal of Operational Research.
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 »
N. Kourentzes, B. Rostami-Tabar and D.K. Barrow, 2017, Journal of Business Research. http://doi.org/10.1016/j.jbusres.2017.04.016
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 »
G. Athanasopoulos, R. J. Hyndman, N. Kourentzes and F. Petropoulos, 2017, European Journal of Operational Research. http://doi.org/10.1016/j.ejor.2017.02.046
I have been looking for a package to do time series modelling in R with neural networks for quite some time with limited success. The only implementation I am aware of that takes care of autoregressive lags in a user-friendly way is the nnetar function in the forecast package, written by Rob Hyndman. In my… Read More »
Y.R. Sagaert, E-H. Aghezzaf, N. Kourentzes and B. Desmet, 2017, Interfaces.
Yes and… no! First, I should say that I am thinking of the common types of neural networks that are comprised by neurons that use some type of sigmoid transfer function, although the arguments discussed here are applicable to other types of neural networks. Before answering the question, let us first quickly summarise how typical… Read More »
A couple of days ago it was the graduation ceremony for MSc and PhD students at Lancaster University. Ivan Svetunkov, one of my ex-PhD students officially graduated; well done Ivan! In a previous post I described briefly part of his research. He is also working on the excellent smooth package for R. You can find… Read More »