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https://www.um.edu.mt/library/oar/handle/123456789/30810| Title: | Predicting the production of total industry in Greece with chaos theory and neural networks |
| Authors: | Hanias, Michael P. Magafas, Lykourgos |
| Keywords: | Economic forecasting -- Greece Neural networks (Computer science) -- Industrial applications Chaotic behavior in systems Industries -- Greece |
| Issue Date: | 2013 |
| Publisher: | University of Piraeus. International Strategic Management Association |
| Citation: | Hanias, M. P., & Magafas, L. (2013). Predicting the production of total industry in Greece with chaos theory and neural networks. European Research Studies Journal, 16(2), 59-67. |
| Abstract: | This paper explores the use of chaos theory, as well as the neural networks, for predicting the Production of Total Industry in Greece. We have found that our data (from 1961 up to 2011) obey to the chaos theory. More specifically, the results from evaluation show that the minimum emending dimension is 4 suggesting chaos with a high dimensionality. We have also found that it is predictable the behavior of this production in the near future. The same results were evaluated using neural network, confirming our prediction. |
| URI: | https://www.um.edu.mt/library/oar//handle/123456789/30810 |
| Appears in Collections: | European Research Studies Journal, Volume 16, Issue 2 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ERSJ,_16(2)_-_A4.pdf | 496.67 kB | Adobe PDF | View/Open |
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