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Time series forecasting using generative AI: leveraging AI for precision forecasting

By:

Vishwas, Banglore Vijay Kumar

Contributor(s):

Macharla, Sri Ram

Material type: Text

TextPublication details: New York Apress 2025Description: xvi, 215 pISBN:

9798868812750

Subject(s):

DDC classification:

006.31 VIS

Summary: 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. 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)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 VIS (Browse shelf(Opens below))	1	Available		009187

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Table 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]

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.

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)

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