| dc.contributor.author | Özdemir, Özer | |
| dc.contributor.author | Memmedli, Memmedaga | |
| dc.contributor.editor | AidaZade, K | |
| dc.date.accessioned | 2019-10-18T18:43:36Z | |
| dc.date.available | 2019-10-18T18:43:36Z | |
| dc.date.issued | 2012 | |
| dc.identifier.isbn | 978-1-4673-4502-6 | |
| dc.identifier.uri | https://hdl.handle.net/11421/10346 | |
| dc.description | 4th International Conference on Problems of Cybernetics and Informatics (PCI) -- SEP 12-14, 2012 -- Baku, AZERBAIJAN | en_US |
| dc.description | WOS: 000320337500196 | en_US |
| dc.description.abstract | Fuzzy time series models have become important in past decades with neural networks. Hence, this study aims to improve forecasting performance of neural network based fuzzy time series by using an optimization function to interval length which affects forecasting accuracy. So, a new approach for improving forecasting performance of neural network-based fuzzy time series is applied with optimization process. The empirical results show that the model with proposed approach by optimization of interval length outperforms other forecasting models proposed in the literature. | en_US |
| dc.description.sponsorship | IEEE | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Fuzzy Time Series | en_US |
| dc.subject | Neural Networks | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | Interval Length | en_US |
| dc.title | Optimization of Interval Length for Neural Network Based Fuzzy Time Series | en_US |
| dc.type | conferenceObject | en_US |
| dc.relation.journal | 2012 Iv International Conference Problems of Cybernetics and Informatics (Pci) | en_US |
| dc.contributor.department | Anadolu Üniversitesi | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
