The PhD Student Council at the University of Skövde hosted a panel discussion on the topic of AI in research. Together with Peter Anderberg, Jörgen Hansson, Amos Ng, and Jane Synnergren, we discussed our views, informed by the perspectives from our different areas of research. We touched upon various aspects of the topic, and concluded… Read More: Discussion panel on ‘AI in research’ at Skövde »
Together with Devon Barrow and Sven Crone, we gave a talk at the recent OR 62 conference, moderated by Christina Phillips. The topic was: “The quest for greater forecasting accuracy: Perspectives from Statistics & Machine Learning”. I have worked with both Devon and Sven in the past years and the three of us share quite… Read More: OR62 -The quest for greater forecasting accuracy: Perspectives from Statistics… »
A few weeks ago I gave a talk at Amazon’s 2019 AMLC in Seattle. The talk was focused on current research in temporal and cross-temporal hierarchies. People who have been following my blog will be familiar with the topic and recent advances. This talk is different in the sense that it does not go in… Read More: Forecasting keynote at AMLC 2019 »
The nnfor (development version here) package for R facilitates time series forecasting with Multilayer Perceptrons (MLP) and Extreme Learning Machines (ELM). Currently (version 0.9.6) it does not support deep learning, though the plan is to extend this to this direction in the near future. Currently, it relies on the neuralnet package for R, which provides… Read More: Tutorial for the nnfor R package »
A feature that was missing in the nnfor package was the ability to reuse and retrain mlp and elm model fits. This is now possible with the new arguments model and retrain. As an example, let us use the AirPassengers time series, with three different sample sizes and re-use and re-train the same model in… Read More: Update for nnfor: reuse and retrain models »
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: Related PostsMAPAx available for R & new MAPA package on CRAN MAPA package… Read More: nnfor on github »
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: New R package nnfor: time series forecasting with neural networks »
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: Forecasting time series with neural networks in R »
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: Can neural networks predict trended time series? »
D. Barrow and N. Kourentzes, 2016, European Journal of Operational Research. http://dx.doi.org/10.1016/j.ejor.2016.07.015