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010 a| 2022045955;

020 a| 9780367321789q| (hbk);

020 a| 9781032418865q| (pbk);

020 z| 9780429321252q| (ebk);

035 a| (WaSeSS)ssj0002824178;

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100 1 a| Gharehbaghi, Arash,d| 1972-?| UNAUTHORIZED;

245 10 a| Deep learning in time series analysish| [electronic resource] /c| Arash Gharehbagh.;

250 a| First edition.;

260 a| Boca Raton :b| CRC Press, Taylor & Francis Group,c| 2023.;

300 a| xi, 195 pages :b| illustrations (some color) ;c| 25 cm;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| "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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Time-series analysisx| Data processing.=| ^A1243340;

650 0 a| Deep learning (Machine learning)=| ^A1458889;

655 0 a| Electronic books.=| ^A491897;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3604722&scope=site&custid=s5822723&authtype=ip,shib;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6751152-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 7/16/2026x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6751152-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/16/2026x| EBOOKz| JERESOURCE;

Deep learning in time series analysis / Arash Gharehbagh.

Author/creator	Gharehbaghi, Arash, 1972-
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton : CRC Press, Taylor & Francis Group, 2023.
Description	xi, 195 pages : illustrations (some color) ; 25 cm
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Time-series analysis--Data processing. Deep learning (Machine learning)

Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2022045955
ISBN	9780367321789 (hbk)
ISBN	9781032418865 (pbk)
ISBN	(ebk)

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Deep learning in time series analysis / Arash Gharehbagh.

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