dc.contributor.author | Bal, Çağatay | |
dc.contributor.author | Aladağ, Çağdaş Hakan | |
dc.date.accessioned | 2022-12-21T12:00:57Z | |
dc.date.available | 2022-12-21T12:00:57Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Bal, Ç. and Ç. H. Aladağ. 2022. "Time Series Modeling with Deep Neural Networks." In Modeling and Advanced Techniques in Modern Economics, 187-209. doi:10.1142/q0346_0009. | en_US |
dc.identifier.issn | 978-180061175-7 / 978-180061174-0 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12809/10449 | |
dc.description.abstract | Deep neural networks are the latest among powerful artificial intelligence tools. As advanced forms of artificial neural networks, deep nets can be used in various fields and also time series forecasting. Time series forecasting is a major domain which extends to almost all problem-wise applications. Because of this reason, powerful tools as deep networks have become the perfect tools with their modular structure for time series forecasting. In this study, starting from shallow neural networks to advanced deep networks, including convolutional nets and long short-term memories, in-depth analytics are investigated and their results are given with applications and Python codes. | en_US |
dc.item-language.iso | eng | en_US |
dc.publisher | World Scientific Publishing Co. | en_US |
dc.relation.isversionof | 10.1142/q0346_0009 | en_US |
dc.item-rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Neural networks | en_US |
dc.title | Time Series Modeling with Deep Neural Networks | en_US |
dc.item-type | bookPart | en_US |
dc.contributor.department | MÜ, Fen Fakültesi, İstatistik Bölümü | en_US |
dc.contributor.authorID | 0000-0002-7823-2712 | en_US |
dc.contributor.institutionauthor | Bal, Çağatay | |
dc.identifier.startpage | 187 | en_US |
dc.identifier.endpage | 209 | en_US |
dc.relation.journal | Modeling and Advanced Techniques in Modern Economics | en_US |
dc.relation.publicationcategory | Kitap Bölümü - Uluslararası | en_US |