Time Series Forecasting Using Generative Ai

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Time Series Forecasting Using Generative Ai

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Author : Vishwas B V
language : en
Publisher: Apress
Release Date : 2025-04-07

Time Series Forecasting Using Generative Ai written by Vishwas B V and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-07 with Computers categories.

"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.

Tims Orsa Bulletin

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Author : Institute of Management Sciences
language : en
Publisher:
Release Date : 1989

Tims Orsa Bulletin written by Institute of Management Sciences and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Industrial management categories.

Time Series Forecasting Using Generative Adversarial Networks

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Author : Sone Sharon
language : en
Publisher:
Release Date : 2023

Time Series Forecasting Using Generative Adversarial Networks written by Sone Sharon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

Time series data is prevalent in many fields, such as finance, weather forecasting, and economics. Predicting future values of a time series can provide valuable insights for decision-making, such as identifying trends, detecting anomalies, and improving resource allocation. Recently, Generative Adversarial Networks (GANs) have been used to learn from these features to aid in time-series forecasting. We propose a novel framework that utilizes the unsupervised paradigm of a GAN based on related research called TimeGAN. Instead of using the discriminator as a classification model, we employ it as a regressive model to learn both temporal and static features. This framework can help generate synthetic data and facilitate forecasting. Our model outperforms TimeGAN, which only preserves temporal dynamics and uses the discriminator as a classifier to distinguish between synthetic and real datasets.

Deep Learning For Time Series Cookbook

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Author : Vitor Cerqueira
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-29

Deep Learning For Time Series Cookbook written by Vitor Cerqueira and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.

Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes Key Features Learn the fundamentals of time series analysis and how to model time series data using deep learning Explore the world of deep learning with PyTorch and build advanced deep neural networks Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.What you will learn Grasp the core of time series analysis and unleash its power using Python Understand PyTorch and how to use it to build deep learning models Discover how to transform a time series for training transformers Understand how to deal with various time series characteristics Tackle forecasting problems, involving univariate or multivariate data Master time series classification with residual and convolutional neural networks Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs) Who this book is for If you’re a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.

Incomplete Time Series Forecasting Using Generative Neural Networks

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Author : Harshit Tarun Shah (First Name)
language : en
Publisher:
Release Date : 2021

Incomplete Time Series Forecasting Using Generative Neural Networks written by Harshit Tarun Shah (First Name) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Dealing with missing data is a long pervading problem and it becomes more challenging when forecasting time series data because of the complex relation between data and time, which is why incomplete data can lead to unreliable results.While some general purpose methods like mean, zero, or median imputation can be employed to alleviate the problem, they might disrupt the inherent structure and the underlying data distributions. Another problem associated with conventional time series forecasting methods whose goal is to predict mean values is that they might sometimes overlook the variance or fluctuations in the input data and eventually lead to faulty predictions. To address these issues, we employ a probabilistic forecasting technique which can accommodate the variations in data and predict a full conditional probability distribution of future values given past data. We introduce a novel generative adversarial network (GAN) architecture with the goal to forecast a probability distribution on time series data and also introduce an auxiliary GAN which learns the temporal pattern of which data is missing, thereby removing the dependency on using general purpose imputation methods. We create two complex time series datasets to test our architecture and also show a comparison between our architecture's forecasting capability (with incomplete data) to a state-of-the art architecture which is trained with complete data. We also demonstrate that our model's predicted data distribution does not collapse with incomplete data, but instead successfully learns to estimate the true underlying data distribution.

Ai Driven Time Series Forecasting

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Author : Raghurami Reddy Etukuru Ph.D.
language : en
Publisher: iUniverse
Release Date : 2023-10-06

Ai Driven Time Series Forecasting written by Raghurami Reddy Etukuru Ph.D. and has been published by iUniverse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Computers categories.

When you enter the world of time series analysis, you step into a labyrinth of numerical patterns, where each turn you take unveils another layer of complexity. Here, simple mathematical or statistical models struggle to keep pace. Reality is riddled with complex patterns in time series data, which, like cryptic pieces of a jigsaw puzzle, hold the key to unraveling insightful predictions. These complex patterns include non-linearity, non-stationarity, long memory or dependence, asymmetry, and stochasticity. But what creates these intricate patterns? Raghurami Reddy Etukuru, Ph.D., a distinguished and adaptable specialist in data science and artificial intelligence, delves into that question in this groundbreaking book, explaining that the factors are numerous and multifaceted, each adding their own measure of challenge. He doesn't just discuss problems but also addresses the forecasting of time series amidst intricate patterns. Take a deep dive deep into the world of numbers and patterns, so you can unravel complexities and leverage the power of artificial intelligence to enhance predictive capabilities. More than just a theoretical guide, this book is a practical companion in the often-turbulent journey of understanding and predicting complex time series data.

Forecasting With Artificial Intelligence

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Author : Mohsen Hamoudia
language : en
Publisher: Springer Nature
Release Date : 2023-09-20

Forecasting With Artificial Intelligence written by Mohsen Hamoudia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-20 with Business & Economics categories.

This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field. The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.

Index To Theses With Abstracts Accepted For Higher Degrees By The Universities Of Great Britain And Ireland And The Council For National Academic Awards

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Author :
language : en
Publisher:
Release Date : 2007

Index To Theses With Abstracts Accepted For Higher Degrees By The Universities Of Great Britain And Ireland And The Council For National Academic Awards written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Dissertations, Academic categories.

Using Artificial Neural Networks For Timeseries Smoothing And Forecasting

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Author : Jaromír Vrbka
language : en
Publisher: Springer Nature
Release Date : 2021-09-04

Using Artificial Neural Networks For Timeseries Smoothing And Forecasting written by Jaromír Vrbka and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-04 with Technology & Engineering categories.

The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.

Time Series Forecasting Using Foundation Models

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Author : Marco Peixeiro
language : en
Publisher: Simon and Schuster
Release Date : 2025-12-16

Time Series Forecasting Using Foundation Models written by Marco Peixeiro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-16 with Computers categories.

Make accurate time series predictions with powerful pretrained foundation models! You don’t need to spend weeks—or even months—coding and training your own models for time series forecasting. Time Series Forecasting Using Foundation Models shows you how to make accurate predictions using flexible pretrained models. In Time Series Forecasting Using Foundation Models you will discover: • The inner workings of large time models • Zero-shot forecasting on custom datasets • Fine-tuning foundation forecasting models • Evaluating large time models Time Series Forecasting Using Foundation Models teaches you how to do efficient forecasting using powerful time series models that have already been pretrained on billions of data points. You’ll appreciate the hands-on examples that show you what you can accomplish with these amazing models. Along the way, you’ll learn how time series foundation models work, how to fine-tune them, and how to use them with your own data. About the technology Time-series forecasting is the art of analyzing historical, time-stamped data to predict future outcomes. Foundational time series models like TimeGPT and Chronos, pre-trained on billions of data points, can now effectively augment or replace painstakingly-built custom time-series models. About the book Time Series Forecasting Using Foundation Models explores the architecture of large time models and shows you how to use them to generate fast, accurate predictions. You’ll learn to fine-tune time models on your own data, execute zero-shot probabilistic forecasting, point forecasting, and more. You’ll even find out how to reprogram an LLM into a time series forecaster—all following examples that will run on an ordinary laptop. What's inside • How large time models work • Zero-shot forecasting on custom datasets • Fine-tuning and evaluating foundation models About the reader For data scientists and machine learning engineers familiar with the basics of time series forecasting theory. Examples in Python. About the author Marco Peixeiro builds cutting-edge open-source forecasting Python libraries at Nixtla. He is the author of Time Series Forecasting in Python. Table of Contents Part 1 1 Understanding foundation models 2 Building a foundation model Part 2 3 Forecasting with TimeGPT 4 Zero-shot probabilistic forecasting with Lag-Llama 5 Learning the language of time with Chronos 6 Moirai: A universal forecasting transformer 7 Deterministic forecasting with TimesFM Part 3 8 Forecasting as a language task 9 Reprogramming an LLM for forecasting Part 4 10 Capstone project: Forecasting daily visits to a blog Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.