Home > Research > Publications & Outputs > Data driven fitting sample selection for time s...
View graph of relations

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

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

Published
Publication date2012
Host publicationThe 2012 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-4673-1489-3
ISBN (Print)978-1-4673-1488-6
Original languageEnglish

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.