LEADER 01236nam a2200337 i 4500 001 991001197109707536 005 20020507112739.0 008 941010s1993 uk ||| | eng 020 $a0521373204 035 $ab10187157-39ule_inst 035 $aLE00643769$9ExL 040 $aDip.to Fisica$bita 084 $a53.1.4 084 $a53.3.11 084 $a530.143 084 $aQC174.45 100 1 $aKorepin, V.E.$0463619 245 10$aQuantum inverse scattering method and correlation functions /$cV.E. Korepin, N.M. Bogoliubov, A.G. Izergin 260 $aCambridge ; New York :$bCambridge University Press,$c1993 300 $axix, 555 p. ;$c26 cm. 490 0 $aCambridge monographs on mathematical physics 500 $aIncludes bibliographical references (p. 520-553) and index. 650 4$aCorrelation 700 1 $aIzergin, A.G. 700 1 $aBogoliubov, N.N. 907 $a.b10187157$b17-02-17$c27-06-02 912 $a991001197109707536 945 $aLE006 53.3.11 KOR$g1$i2006000084253$lle006$o-$pE0.00$q-$rl$s- $t0$u7$v0$w7$x0$y.i10230427$z27-06-02 996 $aQuantum inverse scattering method and correlation functions$9190312 997 $aUNISALENTO 998 $ale006$b01-01-94$cm$da $e-$feng$guk $h0$i1 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,$d[2025] 210 4$dİ2025 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