Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/28208
Title: | Deep learning for time series forecasting | Authors: | Yalçın Kayacan, Eda | Keywords: | Deep Learning, Time Series Forecasting, Multilayer Perceptrons, Convolutional Neural Networks, Long Short-Term Memory Networks | Publisher: | Peter Lang | Abstract: | Time series generally have the characteristics such as high noise, non-linear and chaotic. Due to the characteristics of the time series and the existence of big data, it has been becoming to prefer intelligent methods such as deep learning.The aim of this study is to make estimations of time series using deep learning techniques on financial time series. The originality of study is that the stock prices of Borsa Istanbul-100 forecast using popular three methods about deep learning such as Multilayer Perceptrons, Convolutional Neural Networks and Long Short-Term Memory Networks. | URI: | https://hdl.handle.net/11499/28208 https://doi.org/10.3726/b15875 |
ISBN: | 978-3-631-79568-2 |
Appears in Collections: | Fen-Edebiyat Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
13.pdf Restricted Access | bookpart | 765.61 kB | Adobe PDF | View/Open |
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.