| dc.contributor.author | Aladag C.H. | |
| dc.contributor.author | Eğrioğlu E. | |
| dc.contributor.author | Kadilar C. | |
| dc.date.accessioned | 2020-06-21T09:28:35Z | |
| dc.date.available | 2020-06-21T09:28:35Z | |
| dc.date.issued | 2012 | |
| dc.identifier.isbn | 9.78161E+12 | |
| dc.identifier.uri | https://doi.org/10.2174/978160805373511201010108 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12712/4339 | |
| dc.description.abstract | Forecast combination is a method used for obtaining more accurate forecasts. Forecast combination consists of the combination of forecasts obtained from different models with various methods. There are several types of forecast combination in the forecasting literature. In this study, various fuzzy time series approaches are applied to Turkey's daily highest gold prices series and forecasts obtained from these approaches are combined with variance covariance method (VCM), mean square forecast error method (MSFE) and artificial neural networks (ANN) approach. Results obtained from all of these methods are analyzed and the optimal forecast technique for Turkey's daily highest gold prices series is determined. © 2012 Bentham Science Publishers. All rights reserved. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Bentham Science Publishers Ltd. | en_US |
| dc.relation.isversionof | 10.2174/978160805373511201010108 | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Artificial neural networks | en_US |
| dc.subject | Forecast combination | en_US |
| dc.subject | Forecasting | en_US |
| dc.subject | Fuzzy time series | en_US |
| dc.title | Forecasting gold prices series in turkey by the forecast combination | en_US |
| dc.type | bookPart | en_US |
| dc.contributor.department | OMÜ | en_US |
| dc.identifier.startpage | 108 | en_US |
| dc.identifier.endpage | 117 | en_US |
| dc.relation.journal | Advances in Time Series Forecasting | en_US |
| dc.relation.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
