Time Series Forecasting Using Generative Ai
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Time Series Forecasting Using Generative AI
"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 concepts, 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, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, and PatchTST, for time series forecasting. Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. 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.
Time Series Forecasting Using Generative AI
Author: Banglore Vijay Kumar Vishwas
language: en
Publisher: Springer Nature
Release Date: 2025-03-24
"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. !-- [if !supportLists]--● !--[endif]--Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. !-- [if !supportLists]--● !--[endif]--Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. !-- [if !supportLists]--● !--[endif]--Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. !-- [if !supportLists]--● !--[endif]--Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. !-- [if !supportLists]--● !--[endif]--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.
Deep Learning with Python
Author: Francois Chollet
language: en
Publisher: Simon and Schuster
Release Date: 2017-11-30
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance