Tag Archives: neural networks

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: Related PostsMAPAx example for R Principles of Business Forecasting 2e Benchmarking Facebook’s Prophet

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 »

Forecasting competition: Computational Intelligence in Forecasting

There is a new forecasting competition announced, the International Time Series Forecasting Competition “Computational Intelligence in Forecasting” CIF 2016. The competition is organised by Martin Stepnicka and Michal Burda within IEEE WCCI 2016 congress and it is related to a special session IJCNN-13 Advances in Computational Intelligence for Applied Time Series Forecasting (ACIATSF). For more… Read More »

Time series forecasting competition with computational intelligence methods

I recently became aware of a new forecasting competition: “International Forecasting Competition – Computational Intelligence in Forecasting”. The competition involves forecasting 91 time series of annual, quarterly, monthly and daily sampling frequency of various lengths. Although the competition is focused on computational intelligence methods (incl. fuzzy method, artificial neural networks, evolutionary algorithms, decision & regression… Read More »

Ensemble size and combination operators

Combining forecasts has been shown in many cases to lead to improvements in forecasting performance, in terms of accuracy and bias. This is also common in forecasting with neural networks or other computationally intensive methods, where ensemble forecasts are considered more accurate than individual model forecasts. A useful feature of forecast combination is that it… Read More »