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A variety of resources exist, publishing information on the application of NN in forecasting. However, very few books are dedicated to this topic, focussing either on the technical specification of neural networks as a method, or the forecasting domain.
On the following pages please find some references to the most recommended publications to build the necessary skills for forecasting with artificial neural networks. They incorporate general introductions to Neural Networks or Forecasting as well as specific Literature on Forecasting with NN, although there are very few dedicated publications to date. In any way, if you want to learn about NN in forecasting, you need to essentially understand both: Neural Network design, training and evaluation as well as forecasting domain.
How to Start reading into the field ... (a personal view)
If you don't have time to indulge on reading - you are doing something very wrong - but here is the shortest way in. And NO there is no way around it.
1. Read introduction papers
| Zhang, G., B. Eddy Patuwo, et al. (1998). "Forecasting with artificial neural networks: The state of the art." International Journal of Forecasting 14(1): 35-62. |
| Abstract: Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. Our purpose is to provide (1) a synthesis of published research in this area, (2) insights on ANN modelling issues, and (3) the future research directions. |
In 06/2004 it was still free for download at the top 10 requested papers at Elsevier www.sciencedirect.com !!! Unfortunately, you will already need to understand neural networks to appreciate it fully. So now get to know NN and forecasting. This is your next step:
2. Read relevant BOOKS
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Neural Smithing: Supervised Learning in
Feedforward Artificial Neural Networks by Russell D. Reed, Robert J. Marks II |
| Most books on the field give a very objective approach to connectionism, and describe mathematical knowledge for getting Neural Networks to train. This work of art approaches the problem from a practical point of view, discussing issues that anyone would be faced with when working with Neural Networks. Things like setting learning rates, using stochastic approximation, adding momentum and deciding training times are all key factors that are discussed in depth. The visual aids are extremely helpful, and allow the reader to develop a fell and intuition for Neural Networks. A definite must for neural optimisation fanatics! |
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Forecasting : Methods and Applications by Spyros G. Makridakis, Steven C. Wheelwright, Rob J Hyndman |
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The definite
BIBLE introduction to forecasting! A must read! Datasets: [here] |
3. Reread Papers noted under 1!
4. Read FQ from Neural Networks Newsgroup
| AI-faq/neural-nets |
| Answer to certain questions and topics that come up frequently in the Neural Nets discussion groups, hosted by SAS |
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