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A deep learning framework for financial time series using stacked autoencoders and long-short term memory
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock p...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2017
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510866/ https://www.ncbi.nlm.nih.gov/pubmed/28708865 http://dx.doi.org/10.1371/journal.pone.0180944 |