MARC details
| 000 -LEADER |
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| fixed length control field |
04089nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251025113039.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251025b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9798868812750 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
VIS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Vishwas, Banglore Vijay Kumar |
| 245 ## - TITLE STATEMENT |
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| Title |
Time series forecasting using generative AI: |
| Remainder of title |
leveraging AI for precision forecasting |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
Apress |
| Date of publication, distribution, etc. |
2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvi, 215 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
49.99 |
| 500 ## - GENERAL NOTE |
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| General note |
Table of contents:<br/>Front Matter<br/>Pages i-xvi<br/>Download chapter PDF <br/>Time Series Meets Generative AI<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 1-16<br/>Neural Networks for Time Series<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 17-81<br/>Transformers for Time Series<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 83-130<br/>Time-LLM: Reprogramming Large Language Model<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 131-154<br/>Chronos: Pre-trained Probabilistic Time Series Model<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 155-167<br/>TimeGPT: The First Foundation Model for Time Series<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 169-182<br/>MOIRAI: A Time Series LLM for Universal Forecasting<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 183-194<br/>TimesFM: Time Series Forecasting Using Decoder-Only Foundation Model<br/>Banglore Vijay Kumar Vishwas, Sri Ram Macharla<br/>Pages 195-210<br/>Back Matter<br/>Pages 211-215<br/><br/>[https://link.springer.com/book/10.1007/979-8-8688-1276-7] |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. <br/><br/>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.<br/><br/>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.<br/><br/>● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.<br/><br/>● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.<br/><br/>● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.<br/><br/>● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.<br/><br/>● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.<br/><br/>Who this book is for:<br/><br/>Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.<br/><br/>(https://link.springer.com/book/10.1007/979-8-8688-1276-7) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Macharla, Sri Ram |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
