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Frequency independent automatic input variable selection for neural networks for forecasting

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Frequency independent automatic input variable selection for neural networks for forecasting. / Kourentzes, Nikolaos; Crone, Sven F.
The 2010 International Joint Conference on Neural Networks (IJCNN) . New York: IEEE, 2010. p. -.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Kourentzes N, Crone SF. Frequency independent automatic input variable selection for neural networks for forecasting. In The 2010 International Joint Conference on Neural Networks (IJCNN) . New York: IEEE. 2010. p. - doi: 10.1109/IJCNN.2010.5596637

Author

Kourentzes, Nikolaos ; Crone, Sven F. / Frequency independent automatic input variable selection for neural networks for forecasting. The 2010 International Joint Conference on Neural Networks (IJCNN) . New York : IEEE, 2010. pp. -

Bibtex

@inproceedings{e80f839bdb5e4da08bb97dfeb600fac1,
title = "Frequency independent automatic input variable selection for neural networks for forecasting",
abstract = "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.",
author = "Nikolaos Kourentzes and Crone, {Sven F.}",
year = "2010",
doi = "10.1109/IJCNN.2010.5596637",
language = "English",
isbn = "978-1-4244-6917-8",
pages = "--",
booktitle = "The 2010 International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Frequency independent automatic input variable selection for neural networks for forecasting

AU - Kourentzes, Nikolaos

AU - Crone, Sven F.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1109/IJCNN.2010.5596637

DO - 10.1109/IJCNN.2010.5596637

M3 - Conference contribution/Paper

SN - 978-1-4244-6917-8

SP - -

BT - The 2010 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

CY - New York

ER -