{"product_id":"9798868812750","title":"Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting","description":"\u003ch1\u003eTime Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting\u003c\/h1\u003e \u003ch2\u003eVishwas, Banglore Vijay Kumar; Macharla, Sri Ram\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003e\"\u003cem\u003eTime Series Forecasting Using Generative AI\u003c\/em\u003e introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies.\"\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eThe 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. \u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eBuilding 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eThis 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -18.0pt; line-height: normal; margin: 0cm 0cm .0001pt 36.0pt;\"\u003e\u003cspan style=\"font-size: 10.0pt; font-family: 'Noto Sans Symbols',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-bidi-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e●\u003c\/span\u003e\u003cspan style=\"font-size: 7.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e       \u003c\/span\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eUnderstand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -18.0pt; line-height: normal; margin: 0cm 0cm .0001pt 36.0pt;\"\u003e\u003cspan style=\"font-size: 10.0pt; font-family: 'Noto Sans Symbols',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-bidi-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e●\u003c\/span\u003e\u003cspan style=\"font-size: 7.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e       \u003c\/span\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eLearn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -18.0pt; line-height: normal; margin: 0cm 0cm .0001pt 36.0pt;\"\u003e\u003cspan style=\"font-size: 10.0pt; font-family: 'Noto Sans Symbols',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-bidi-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e●\u003c\/span\u003e\u003cspan style=\"font-size: 7.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e       \u003c\/span\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eDiscover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -18.0pt; line-height: normal; margin: 0cm 0cm .0001pt 36.0pt;\"\u003e\u003cspan style=\"font-size: 10.0pt; font-family: 'Noto Sans Symbols',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-bidi-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e●\u003c\/span\u003e\u003cspan style=\"font-size: 7.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e       \u003c\/span\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eExplore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"text-indent: -18.0pt; line-height: normal; margin: 0cm 0cm .0001pt 36.0pt;\"\u003e\u003cspan style=\"font-size: 10.0pt; font-family: 'Noto Sans Symbols',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-bidi-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e●\u003c\/span\u003e\u003cspan style=\"font-size: 7.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Noto Sans Symbols'; mso-fareast-language: EN-IN;\"\u003e       \u003c\/span\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eGain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eWho this book is for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: EN-IN;\"\u003eData Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-03-25\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9798868812750\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/979-8-8688-1276-7\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 215\u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":45385874145420,"sku":"9798868812750","price":44.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9798868812750.jpg?v=1776874168","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9798868812750","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}