Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting
暫譯: 使用生成式 AI 進行時間序列預測：利用 AI 實現精確預測
Vishwas, Banglore Vijay Kumar, Macharla, Sri Ram
相關主題
商品描述
"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.
Who this book is for:
Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
商品描述(中文翻譯)
《使用生成式人工智慧進行時間序列預測》向讀者介紹了生成式人工智慧（Gen AI）在時間序列分析中的應用，提供了對尖端預測方法的基本探索。
本書涵蓋了廣泛的主題，首先介紹生成式人工智慧的概述，讓讀者了解Gen AI的歷史和基本概念，並簡要介紹大型語言模型。隨後的章節解釋了實際應用，指導讀者如何使用現代工具實現多種神經網絡架構進行時間序列分析，例如多層感知器（Multi-Layer Perceptrons, MLP）、WaveNet、時間卷積網絡（Temporal Convolutional Network, TCN）、雙向時間卷積網絡（Bidirectional Temporal Convolutional Network, BiTCN）、遞迴神經網絡（Recurrent Neural Networks, RNN）、長短期記憶（Long Short-Term Memory, LSTM）、深度自回歸（Deep AutoRegressive, DeepAR）和神經基礎擴展分析（Neural Basis Expansion Analysis, NBEATS）。
在此基礎上，本書介紹了Transformer架構的強大功能，探索其變體，如基本Transformer（Vanilla Transformers）、反向Transformer（Inverted Transformer, iTransformer）、DLinear、NLinear和補丁時間序列Transformer（Patch Time Series Transformer, PatchTST）。最後，本書深入探討了基礎模型，如Time-LLM、Chronos、TimeGPT、Moirai和TimesFM，使讀者能夠實施針對其特定需求的複雜預測模型。
本書使讀者具備利用Gen AI進行準確且高效的時間序列預測所需的知識和技能。通過詳細探索先進的預測模型和方法，本書使從業者能夠做出明智的決策，並通過數據驅動的洞察推動業務增長。
● 了解Gen AI的核心歷史和應用及其顛覆時間序列預測的潛力。
● 學習如何實施不同的神經網絡架構，如MLP、WaveNet、TCN、BiTCN、RNN、LSTM、DeepAR和NBEATS進行時間序列預測。
● 探索Transformer架構及其變體（如基本Transformer、iTransformer、DLinear、NLinear和PatchTST）在時間序列預測中的潛力。
● 深入了解複雜的基礎模型，如Time-LLM、Chronos、TimeGPT、Moirai和TimesFM。
● 獲得如何將Gen AI技術應用於現實世界時間序列預測挑戰並做出數據驅動決策的實用知識。
本書的讀者對象：
數據科學家、機器學習工程師、商業分析師、統計學家、經濟學家、金融分析師、運籌學分析師、數據分析師、學生。
作者簡介
Bangalore Vijay Kumar Vishwas (B.V. Vishwas) is a seasoned Principal Data Scientist and AI researcher with over 11 years of experience in the IT industry. Currently based in San Diego, California, he works at NTT DATA. Vishwas holds a Master of Technology in Software Engineering from Birla Institute of Technology & Science, Pilani, India. He specializes in developing innovative solutions for large enterprises, with expertise in Machine Learning, Deep Learning, Time Series Forecasting, Natural Language Processing, Reinforcement Learning, and Generative AI. He is also the author of Hands-On Time Series Analysis with Python: From Basics to Bleeding-Edge Techniques, published by Apress.
Sri Ram Macharla, is a consultant and architect in the areas of AI and ML with over 19 years of experience in IT. He holds an M.Tech from BITS Pilani and has experience working with clients in domains such as finance, retail, life sciences, defense, and manufacturing. Additionally, he has worked as a mentor, corporate trainer, and guest faculty teaching AI and ML. He has papers published and works as a reviewer with leading journals and publishers. He is passionate about mathematical modeling and applying AI for social good.
作者簡介(中文翻譯)
班加羅爾的維賈伊·庫馬爾·維斯瓦斯（B.V. Vishwas）是一位經驗豐富的首席數據科學家和人工智慧研究員，在資訊科技產業擁有超過11年的經驗。目前他居住在加州聖地牙哥，並在NTT DATA工作。維斯瓦斯擁有印度比爾拉科技與科學學院（Birla Institute of Technology & Science, Pilani）的軟體工程碩士學位。他專注於為大型企業開發創新解決方案，擅長機器學習（Machine Learning）、深度學習（Deep Learning）、時間序列預測（Time Series Forecasting）、自然語言處理（Natural Language Processing）、強化學習（Reinforcement Learning）和生成式人工智慧（Generative AI）。他也是《使用Python進行實作時間序列分析：從基礎到前沿技術》（Hands-On Time Series Analysis with Python: From Basics to Bleeding-Edge Techniques）的作者，該書由Apress出版。
斯里·拉姆·馬查拉（Sri Ram Macharla）是一位在人工智慧（AI）和機器學習（ML）領域的顧問和架構師，擁有超過19年的資訊科技經驗。他擁有比爾拉科技與科學學院的碩士學位，並曾與金融、零售、生命科學、國防和製造等領域的客戶合作。此外，他還擔任過導師、企業培訓師和客座講師，教授AI和ML。他發表過多篇論文，並擔任多家領先期刊和出版社的審稿人。他對數學建模充滿熱情，並致力於將AI應用於社會公益。
