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ISBN 10: 6203307092 ISBN 13: 9786203307092
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Language: English
Published by LAP LAMBERT Academic Publishing, 2017
ISBN 10: 6202004401 ISBN 13: 9786202004404
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ISBN 10: 6203148342 ISBN 13: 9786203148343
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ISBN 10: 6204092510 ISBN 13: 9786204092515
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ISBN 10: 6203148717 ISBN 13: 9786203148718
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ISBN 10: 6203148350 ISBN 13: 9786203148350
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Published by Edicoes Nosso Conhecimento, 2021
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Published by LAP LAMBERT Academic Publishing, 2021
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Published by LAP LAMBERT Academic Publishing, 2020
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: NICA IonutIonut NICA (b. May 2, 1992) is a PhD in Cybernetics and Economic Statistics and an assistant professor at the Faculty of Cybernetics, Statistics and Informatics Economics. He works in the bank as a Basel Expert in the depar.
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Published by LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203307092 ISBN 13: 9786203307092
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: NICA IonutIonut NICA, PhD in Cybernetics & Economic Statistics and an Assistant Professor at the Faculty of Cybernetics, Statistics & Informatics Economics. He works in the bank as a Senior Internal Auditor - Specialist in Modeling, .
Language: English
Published by LAP LAMBERT Academic Publishing, 2017
ISBN 10: 6202004401 ISBN 13: 9786202004404
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Popescu Bianca IoanaBianca Ioana Popescu is currently Business Management Advisor at IBM Romania with a PhD in Economic Cybernetics. Has as interests the study of complex adaptive systems, business analysis and macroeconomics. Acts a.
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Add to basketHardcover. Condition: new. Hardcover. This Special Issue, "Advanced Methods for Time Series Forecasting", focuses on recent methodological and applied advances that address the complex modern time series data. As forecasting has been increasingly used in decision-making in various research-related fields such as, but not limited to energy systems, finance, transportation, smart infrastructure, environmental modeling, and management science, the need for models that can capture nonlinear dynamics, long-term dependencies, multiscale patterns, and uncertainty is real and actual.The Special Issue provides an innovative approach to the issues associated with the time series forecasting, spanning artificial intelligence, deep learning, hybrid statistical-machine learning models, attention mechanisms, transformer-based architectures, state-space formulations, and signal decomposition techniques. Contributions featured in the Special Issue include theoretical developments, as well as empirical studies, comparative evaluations, and domain-driven applications which have the role to demonstrate the extent to which the advanced forecasting methods outperform traditional techniques in challenging real-world settings. By bridging together various perspectives and real-life application contexts, this Special Issue aims to provide an overview of current trends, as well as to offer practical solutions, and future research directions in advanced time series forecasting. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Hardcover. Condition: new. Hardcover. This Special Issue, "Advanced Methods for Time Series Forecasting", focuses on recent methodological and applied advances that address the complex modern time series data. As forecasting has been increasingly used in decision-making in various research-related fields such as, but not limited to energy systems, finance, transportation, smart infrastructure, environmental modeling, and management science, the need for models that can capture nonlinear dynamics, long-term dependencies, multiscale patterns, and uncertainty is real and actual.The Special Issue provides an innovative approach to the issues associated with the time series forecasting, spanning artificial intelligence, deep learning, hybrid statistical-machine learning models, attention mechanisms, transformer-based architectures, state-space formulations, and signal decomposition techniques. Contributions featured in the Special Issue include theoretical developments, as well as empirical studies, comparative evaluations, and domain-driven applications which have the role to demonstrate the extent to which the advanced forecasting methods outperform traditional techniques in challenging real-world settings. By bridging together various perspectives and real-life application contexts, this Special Issue aims to provide an overview of current trends, as well as to offer practical solutions, and future research directions in advanced time series forecasting. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Buch. Condition: Neu. Advanced Methods for Time Series Forecasting | Buch | Englisch | 2026 | MDPI AG | EAN 9783725869404 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In einem unsicheren Umfeld, das von einer Vielzahl von Informationen umgeben ist, ist die Bewertung systemischer Ereignisse und die Messung des Schweregrads eines Netzwerks ein sensibles Thema und muss mit grosser Sorgfalt angegangen werden. Die Untersuchung.
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Die Wirtschaft, definiert als Gesamtheit der wirtschaftlichen Aktivitaeten, kann als ein Netzwerk beschrieben werden, dessen Glieder Unternehmen, Banken, oeffentliche Einrichtungen, Bildungseinrichtungen, Nichtregierungsorganisationen usw. sind. Die von all d.