Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/7BC7D3AF-6001-4EDB-A963-FAF779931EC0 | - |
| Identifier | https://www.tandfonline.com/doi/abs/10.1080/10454446.2014.1000445?journalCode=wfpm20 | - |
| Identifier | https://doi.org/10.1080/10454446.2014.1000445 | - |
| Language | en | - |
| Extent | 11 pages | en |
| Title | New technology in shopping: forecasting electronic shopping with the use of a neuro-fuzzy system | en |
| Creator | Atsalakis Georgios | en |
| Creator | Ατσαλακης Γεωργιος | el |
| Publisher | Taylor & Francis | en |
| Content Summary | This article presents the application of neuro-fuzzy techniques in forecasting a new technology in shopping. Neural networks have been used successfully to forecast time series due to their significant properties of treating nonlinear data with self-learning capability. However, neural networks suffer the difficulty of dealing with qualitative information and the “black box” syndrome that more or less limits their applications in practice. To overcome the drawbacks of neural networks, in this study, we proposed a fuzzy neural network that is a class of adaptive networks functionally equivalent to a fuzzy inference system. The results derived from the experiment based on electronic sales indicated that the suggested fuzzy neural network could be an efficient system to forecast a new technology in shopping. Experimental results also show that the neuro-fuzzy approach outperforms the other two conventional models (AR and ARMA). | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-04-27 | - |
| Date of Publication | 2017 | - |
| Subject | ANFIS | en |
| Subject | Electronic shopping forecasting | en |
| Subject | Neuro-fuzzy forecasting | en |
| Subject | New technology forecasting | en |
| Bibliographic Citation | G. Atsalakis, "New technology in shopping: forecasting electronic shopping with the use of a neuro-fuzzy system," J. Food Prod. Market., vol. 23, no. 5, pp. 522-532, Jul. 2017. doi: 10.1080/10454446.2014.1000445 | en |
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