000			04089nam a22002177a 4500
005			20251025113039.0
008			251025b |||||||| |||| 00| 0 eng d
020			_a9798868812750
082			_a006.31 _bVIS
100			_aVishwas, Banglore Vijay Kumar _925839
245			_aTime series forecasting using generative AI: _bleveraging AI for precision forecasting
260			_aNew York _bApress _c2025
300			_axvi, 215 p.
365			_aEURO _b49.99
500			_aTable of contents: Front Matter Pages i-xvi Download chapter PDF Time Series Meets Generative AI Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 1-16 Neural Networks for Time Series Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 17-81 Transformers for Time Series Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 83-130 Time-LLM: Reprogramming Large Language Model Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 131-154 Chronos: Pre-trained Probabilistic Time Series Model Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 155-167 TimeGPT: The First Foundation Model for Time Series Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 169-182 MOIRAI: A Time Series LLM for Universal Forecasting Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 183-194 TimesFM: Time Series Forecasting Using Decoder-Only Foundation Model Banglore Vijay Kumar Vishwas, Sri Ram Macharla Pages 195-210 Back Matter Pages 211-215 [https://link.springer.com/book/10.1007/979-8-8688-1276-7]
520			_aThe 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. Who this book is for: Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students. (https://link.springer.com/book/10.1007/979-8-8688-1276-7)
650			_aMachine learning
650			_aArtificial intelligence
700			_aMacharla, Sri Ram _925840
942			_cBK _2ddc
999			_c10417 _d10417