Deep learning in time series analysis:
"The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original de...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton
CRC Press
2023
|
| Edition: | First edition |
| Subjects: | |
| Online Access: | DE-824 Volltext |
| Summary: | "The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level"-- |
| Item Description: | OCLC-licensed vendor bibliographic record |
| Physical Description: | 1 Online-Ressource |
| ISBN: | 9780429321252 0429321252 9781000911435 1000911438 9781000911404 1000911403 |
| DOI: | 10.1201/9780429321252 |
Staff View
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| illustrated | Not Illustrated |
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| isbn | 9780429321252 0429321252 9781000911435 1000911438 9781000911404 1000911403 |
| language | English |
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| publisher | CRC Press |
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| spelling | Gharehbaghi, Arash 1972- Verfasser aut Deep learning in time series analysis Arash Gharehbagh First edition Boca Raton CRC Press 2023 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier OCLC-licensed vendor bibliographic record "The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level"-- COMPUTERS / Data Modeling & Design bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Time-series analysis / Data processing Deep learning (Machine learning) Erscheint auch als Druck-Ausgabe 0367321785 Erscheint auch als Druck-Ausgabe 1032418869 Erscheint auch als Druck-Ausgabe 9780367321789 Erscheint auch als Druck-Ausgabe 9781032418865 https://doi.org/10.1201/9780429321252 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Gharehbaghi, Arash 1972- Deep learning in time series analysis COMPUTERS / Data Modeling & Design bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Time-series analysis / Data processing Deep learning (Machine learning) |
| title | Deep learning in time series analysis |
| title_auth | Deep learning in time series analysis |
| title_exact_search | Deep learning in time series analysis |
| title_full | Deep learning in time series analysis Arash Gharehbagh |
| title_fullStr | Deep learning in time series analysis Arash Gharehbagh |
| title_full_unstemmed | Deep learning in time series analysis Arash Gharehbagh |
| title_short | Deep learning in time series analysis |
| title_sort | deep learning in time series analysis |
| topic | COMPUTERS / Data Modeling & Design bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Time-series analysis / Data processing Deep learning (Machine learning) |
| topic_facet | COMPUTERS / Data Modeling & Design MATHEMATICS / Probability & Statistics / General Time-series analysis / Data processing Deep learning (Machine learning) |
| url | https://doi.org/10.1201/9780429321252 |
| work_keys_str_mv | AT gharehbaghiarash deeplearningintimeseriesanalysis |