Kourentzes, Nikolaos and Crone, Sven F. (2010) Frequency independent automatic input variable selection for neural networks for forecasting. In: The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, New York, -. ISBN 978-1-4244-6917-8Full text not available from this repository.
Key issue in time series forecasting with Neural Networks (NN) is the selection of the relevant input variables, which is often the result of data exploration by human experts, leading to dataset specific solutions and limiting forecasting automation. This becomes even more important in heterogeneous datasets, where each time series requires special modeling and can exhibit a different variety of stochastic and deterministic components of different unknown frequencies. Fully automated forecasting with NNs requires a methodology that can address these issues in an entirely data driven approach. This paper proposes a fully automated input selection methodology based on a novel iterative NN filter that automatically identifies for each time series the seasonal frequencies, if such are present, the dynamic structure of the time series, distinguishing between stochastic and deterministic components, ultimately producing a parsimonious set of input variables. The robustness and performance of the algorithm are evaluated against established time series forecasting methods.
|Item Type:||Contribution in Book/Report/Proceedings|
|Subjects:||H Social Sciences > HB Economic Theory|
|Departments:||Lancaster University Management School > Management Science|
|Deposited On:||24 Jul 2012 15:05|
|Last Modified:||28 Oct 2016 01:54|
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