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Data driven fitting sample selection for time series forecasting with neural networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Data driven fitting sample selection for time series forecasting with neural networks. / Kourentzes, Nikolaos.
The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, 2012.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Kourentzes, N 2012, Data driven fitting sample selection for time series forecasting with neural networks. in The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE. https://doi.org/10.1109/IJCNN.2012.6252528

APA

Kourentzes, N. (2012). Data driven fitting sample selection for time series forecasting with neural networks. In The 2012 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN.2012.6252528

Vancouver

Kourentzes N. Data driven fitting sample selection for time series forecasting with neural networks. In The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE. 2012 doi: 10.1109/IJCNN.2012.6252528

Author

Kourentzes, Nikolaos. / Data driven fitting sample selection for time series forecasting with neural networks. The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, 2012.

Bibtex

@inproceedings{db52d0e566f44fb18b9a71754074a73d,
title = "Data driven fitting sample selection for time series forecasting with neural networks",
abstract = "In this paper we propose a data driven method to select the fitting sample of neural networks for time series forecasting. In spite of the fundamental importance of sample selection for model building there has been limited research in the forecasting literature, mostly concluding in vague recommendations on how much time series history should be used and stored. This research addresses this issue in a data driven framework. The proposed method allows the neural networks to iteratively adjust the fitting sample, penalizing the time series history for age and inconsistent behavior. The resulting selected sample helps the networks to produce accurate out-of-sample forecasts, focusing on the recent history of the time series. The performance of the method is demonstrated using time series from different domains, exhibiting substantial improvements in accuracy.",
author = "Nikolaos Kourentzes",
year = "2012",
doi = "10.1109/IJCNN.2012.6252528",
language = "English",
isbn = "978-1-4673-1488-6",
booktitle = "The 2012 International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Data driven fitting sample selection for time series forecasting with neural networks

AU - Kourentzes, Nikolaos

PY - 2012

Y1 - 2012

N2 - In this paper we propose a data driven method to select the fitting sample of neural networks for time series forecasting. In spite of the fundamental importance of sample selection for model building there has been limited research in the forecasting literature, mostly concluding in vague recommendations on how much time series history should be used and stored. This research addresses this issue in a data driven framework. The proposed method allows the neural networks to iteratively adjust the fitting sample, penalizing the time series history for age and inconsistent behavior. The resulting selected sample helps the networks to produce accurate out-of-sample forecasts, focusing on the recent history of the time series. The performance of the method is demonstrated using time series from different domains, exhibiting substantial improvements in accuracy.

AB - In this paper we propose a data driven method to select the fitting sample of neural networks for time series forecasting. In spite of the fundamental importance of sample selection for model building there has been limited research in the forecasting literature, mostly concluding in vague recommendations on how much time series history should be used and stored. This research addresses this issue in a data driven framework. The proposed method allows the neural networks to iteratively adjust the fitting sample, penalizing the time series history for age and inconsistent behavior. The resulting selected sample helps the networks to produce accurate out-of-sample forecasts, focusing on the recent history of the time series. The performance of the method is demonstrated using time series from different domains, exhibiting substantial improvements in accuracy.

U2 - 10.1109/IJCNN.2012.6252528

DO - 10.1109/IJCNN.2012.6252528

M3 - Conference contribution/Paper

SN - 978-1-4673-1488-6

BT - The 2012 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

ER -