LEADER 03455nam 2200613 450 001 9910659492603321 005 20231005193617.0 010 $a9783031134838$b(electronic bk.) 010 $z9783031134821 024 7 $a10.1007/978-3-031-13483-8 035 $a(MiAaPQ)EBC7200006 035 $a(Au-PeEL)EBL7200006 035 $a(CKB)26129988000041 035 $a(DE-He213)978-3-031-13483-8 035 $a(PPN)268208824 035 $a(EXLCZ)9926129988000041 100 $a20230510d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClosed Loop Control and Management $eIntroduction to Feedback Control Theory with Data Stream Managers /$fSerge Zacher 205 $a1st ed. 2022. 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (396 pages) 311 08$aPrint version: Zacher, Serge Closed Loop Control and Management Cham : Springer International Publishing AG,c2023 9783031134821 320 $aIncludes bibliographical references. 327 $aClassic closed loop control from Heron till now:- Basics of the closed loop management -- Engineering of closed loops -- Mathematical Backgrounds. 330 $aThe block diagrams as engineering means for closed loop control, which have been established by classic control theory for decades, are replaced in the above mentioned book by networks, the signals are replaced by data. It corresponds to the ?Industry 4.0? and to the structure of today?s automatic control systems. Thereby a classic closed loop is treated not isolated from other elements of nowadays automation like bus communication and process logical control, and is completed in proposed book with new control elements, so called data stream managers (DSM). The proposed book treats the control theory systematically like it is done in classical books considering the new concept of data management. The theory is accompanied in the book with examples, exercises with solutions and MATLAB®-simulations. About the Author: Dr. Serge Zacher is retired university professor of automation and author of many patents, papers and books, including ?Regelungstechnik für Ingenieure? (with M. Reuter), which counts to standard works for German universities of applied sciences. His further books in the Springer-Vieweg are ?Automatisierungstechnik kompakt?, ?Übungsbuch Regelungstechnik?, ?Drei-Bode-Plots-Verfahren? and ?Regelungstechnik mit Data Stream Management?. He is actually a lecturer of automation at the universities of applied sciences Darmstadt and Stuttgart. 606 $aAutomatic control 606 $aAutomation 606 $aControl theory 606 $aControl automàtic$2thub 606 $aTeoria de control$2thub 606 $aTeoria de sistemes$2thub 606 $aAutomatització$2thub 608 $aLlibres electrònics$2thub 615 0$aAutomatic control. 615 0$aAutomation. 615 0$aControl theory. 615 7$aControl automàtic 615 7$aTeoria de control 615 7$aTeoria de sistemes 615 7$aAutomatització 676 $a629.8 700 $aZacher$b Serge$01228738 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910659492603321 996 $aClosed Loop Control and Management$93030794 997 $aUNINA LEADER 04564nam 22005175 450 001 9910988389203321 005 20251024165354.0 010 $a9798868812767$b(ebook) 024 7 $a10.1007/979-8-8688-1276-7 035 $a(CKB)38111234500041 035 $a(DE-He213)979-8-8688-1276-7 035 $a(CaSebORM)9798868812767 035 $a(OCoLC)1511787524 035 $a(OCoLC-P)1511787524 035 $a(MiAaPQ)EBC31974444 035 $a(Au-PeEL)EBL31974444 035 $a(EXLCZ)9938111234500041 100 $a20250325h20252025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime series forecasting using generative AI $eleveraging ai for precision forecasting /$fBanglore Vijay Kumar Vishwas, Sri Ram Macharla 210 1$aNew York :$cApress,$d[2025] 210 4$d©2025 215 $a1 online resource (xvi, 215 pages) $cillustrations 311 08$a9798868812750 320 $aIncludes bibliographical references. 327 $aChapter 1: Time Series Meets Generative AI -- Chapter 2: Neural Network For Time Series -- Chapter 3: Transformers For Time Series -- Chapter 4: Time-LLM: Reprogramming Large Language Model -- Chapter 5: Chronos: Pretrained Probabilistic Time Series Model -- Chapter 6: TimeGPT: The First Foundation Model For Time Series -- Chapter 7: Moirai: A Time Series Foundation Model For Universal Forecasting -- Chapter 8: TimesFM: Decoder-Only Foundation Model For Time Series. 330 $a"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. ? Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. ? Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. ? Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. ? Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. ? Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions. 606 $aTime-series analysis$xData processing 606 $aArtificial intelligence 606 $aForecasting$xData processing 615 0$aTime-series analysis$xData processing. 615 0$aArtificial intelligence. 615 0$aForecasting$xData processing. 676 $a006.31 700 $aVishwas$b Banglore Vijay Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut$01802658 702 $aMacharla$b Sri Ram$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910988389203321 996 $aTime Series Forecasting Using Generative AI$94348719 997 $aUNINA