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Author: Ana Jesus Lopez-Menendez Publisher: MDPI ISBN: 3039364871 Category : Technology & Engineering Languages : en Pages : 200
Book Description
This book shows the potential of entropy and information theory in forecasting, including both theoretical developments and empirical applications. The contents cover a great diversity of topics, such as the aggregation and combination of individual forecasts, the comparison of forecasting performance, and the debate concerning the tradeoff between complexity and accuracy. Analyses of forecasting uncertainty, robustness, and inconsistency are also included, as are proposals for new forecasting approaches. The proposed methods encompass a variety of time series techniques (e.g., ARIMA, VAR, state space models) as well as econometric methods and machine learning algorithms. The empirical contents include both simulated experiments and real-world applications focusing on GDP, M4-Competition series, confidence and industrial trend surveys, and stock exchange composite indices, among others. In summary, this collection provides an engaging insight into entropy applications for forecasting, offering an interesting overview of the current situation and suggesting possibilities for further research in this field.
Author: Ana Jesus Lopez-Menendez Publisher: MDPI ISBN: 3039364871 Category : Technology & Engineering Languages : en Pages : 200
Book Description
This book shows the potential of entropy and information theory in forecasting, including both theoretical developments and empirical applications. The contents cover a great diversity of topics, such as the aggregation and combination of individual forecasts, the comparison of forecasting performance, and the debate concerning the tradeoff between complexity and accuracy. Analyses of forecasting uncertainty, robustness, and inconsistency are also included, as are proposals for new forecasting approaches. The proposed methods encompass a variety of time series techniques (e.g., ARIMA, VAR, state space models) as well as econometric methods and machine learning algorithms. The empirical contents include both simulated experiments and real-world applications focusing on GDP, M4-Competition series, confidence and industrial trend surveys, and stock exchange composite indices, among others. In summary, this collection provides an engaging insight into entropy applications for forecasting, offering an interesting overview of the current situation and suggesting possibilities for further research in this field.
Author: Pedro Filipe Cunha Publisher: Springer Science & Business Media ISBN: 0387498648 Category : Computers Languages : en Pages : 599
Book Description
The first Digital Enterprise Technology (DET) International Conference was held in Durham, UK in 2002 and the second DET Conference in Seattle, USA in 2004. Sponsored by CIRP (College International pour la Recherche en Productique), the third DET Conference took place in Setúbal, Portugal in 2006. Digital Enterprise Technology: Perspectives and Future Challenges is an edited volume based on this conference. Topics include: distributed and collaborative design, process modeling and process planning, advanced factory equipment and layout design and modeling, physical-to-digital environment integrators, enterprise integration technologies, and entrepreneurship in DET.
Author: Vijay Singh Publisher: Springer Science & Business Media ISBN: 9780792352242 Category : Science Languages : en Pages : 400
Book Description
Since the pioneering work of Shannon in the late 1940's on the development of the theory of entropy and the landmark contributions of Jaynes a decade later leading to the development of the principle of maximum entropy (POME), the concept of entropy has been increasingly applied in a wide spectrum of areas, including chemistry, electronics and communications engineering, data acquisition and storage and retreival, data monitoring network design, ecology, economics, environmental engineering, earth sciences, fluid mechanics, genetics, geology, geomorphology, geophysics, geotechnical engineering, hydraulics, hydrology, image processing, management sciences, operations research, pattern recognition and identification, photogrammetry, psychology, physics and quantum mechanics, reliability analysis, reservoir engineering, statistical mechanics, thermodynamics, topology, transportation engineering, turbulence modeling, and so on. New areas finding application of entropy have since continued to unfold. The entropy concept is indeed versatile and its applicability widespread. In the area of hydrology and water resources, a range of applications of entropy have been reported during the past three decades or so. This book focuses on parameter estimation using entropy for a number of distributions frequently used in hydrology. In the entropy-based parameter estimation the distribution parameters are expressed in terms of the given information, called constraints. Thus, the method lends itself to a physical interpretation of the parameters. Because the information to be specified usually constitutes sufficient statistics for the distribution under consideration, the entropy method provides a quantitative way to express the information contained in the distribution.
Author: Vijay P. Singh Publisher: Wiley-Blackwell ISBN: 9781119976561 Category : Science Languages : en Pages : 662
Book Description
Entropy Theory and its Application in Environmental and Water Engineering responds to the need for a book that deals with basic concepts of entropy theory from a hydrologic and water engineering perspective and then for a book that deals with applications of these concepts to a range of water engineering problems. The range of applications of entropy is constantly expanding and new areas finding a use for the theory are continually emerging. The applications of concepts and techniques vary across different subject areas and this book aims to relate them directly to practical problems of environmental and water engineering. The book presents and explains the Principle of Maximum Entropy (POME) and the Principle of Minimum Cross Entropy (POMCE) and their applications to different types of probability distributions. Spatial and inverse spatial entropy are important for urban planning and are presented with clarity. Maximum entropy spectral analysis and minimum cross entropy spectral analysis are powerful techniques for addressing a variety of problems faced by environmental and water scientists and engineers and are described here with illustrative examples. Giving a thorough introduction to the use of entropy to measure the unpredictability in environmental and water systems this book will add an essential statistical method to the toolkit of postgraduates, researchers and academic hydrologists, water resource managers, environmental scientists and engineers. It will also offer a valuable resource for professionals in the same areas, governmental organizations, private companies as well as students in earth sciences, civil and agricultural engineering, and agricultural and rangeland sciences. This book: Provides a thorough introduction to entropy for beginners and more experienced users Uses numerous examples to illustrate the applications of the theoretical principles Allows the reader to apply entropy theory to the solution of practical problems Assumes minimal existing mathematical knowledge Discusses the theory and its various aspects in both univariate and bivariate cases Covers newly expanding areas including neural networks from an entropy perspective and future developments.
Author: Huijuan Cui Publisher: MDPI ISBN: 3038972223 Category : Technology & Engineering Languages : en Pages : 512
Book Description
Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Rényi, Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.
Author: Vijay P. Singh Publisher: McGraw Hill Professional ISBN: 0071835474 Category : Technology & Engineering Languages : en Pages : 849
Book Description
A THOROUGH INTRODUCTION TO ENTROPY THEORY AND ITS APPLICATIONS IN HYDROLOGIC SCIENCE AND ENGINEERING This comprehensive volume addresses basic concepts of entropy theory from a hydrologic engineering perspective. The application of these concepts to a wide range of hydrologic engineering problems is discussed in detail. The book is divided into sections--preliminaries, rainfall and evapotranspiration, subsurface flow, surface flow, and environmental considerations. Helpful equations, solutions, tables, and diagrams are included throughout this practical resource. Entropy Theory in Hydrologic Science and Engineering covers: Introduction to entropy theory Maximum entropy production principle Performance measures Morphological analysis Evaluation and design of sampling and measurement networks Precipitation variability Rainfall frequency distributions Evaluation of precipitation forecasting schemes Assessment of potential water resources availability Evaporation Infiltration Soil moisture Groundwater flow Rainfall-runoff modeling Streamflow simulation Hydrologic frequency analysis Streamflow forecasting River flow regime classification Sediment yield Eco-index
Author: Hongjun Guan Publisher: Infinite Study ISBN: Category : Mathematics Languages : en Pages : 18
Book Description
In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data.
Author: Manu Joseph Publisher: Packt Publishing Ltd ISBN: 1835883192 Category : Computers Languages : en Pages : 659
Book Description
Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Key Features Apply ML and global models to improve forecasting accuracy through practical examples Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions Purchase of the print or Kindle book includes a free eBook in PDF format Book Description Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills. What you will learn Build machine learning models for regression-based time series forecasting Apply powerful feature engineering techniques to enhance prediction accuracy Tackle common challenges like non-stationarity and seasonality Combine multiple forecasts using ensembling and stacking for superior results Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series Evaluate and validate your forecasts using best practices and statistical metrics Who this book is for This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.
Author: Rajeev R. Raje Publisher: Springer Nature ISBN: 981166448X Category : Computers Languages : en Pages : 656
Book Description
This book constitutes refereed proceedings of the 3rd International Conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies. This book covers a wide range of topics—vision, analytics, robotics, networking, health care, current pandemic issues of COVID-19, and cutting-edge technologies connected to cybersecurity in digital manufacturing and Industry 4.0. The contents of this book will be useful to researchers from industry and academia. The volume includes novel contributions and the latest developments from researchers across industry and academia. The book will serve as a valuable reference resource for academics and researchers across the globe.
Author: Nishchal K. Verma Publisher: Springer ISBN: 9811311358 Category : Technology & Engineering Languages : en Pages : 660
Book Description
This book presents selected proceedings of ICCI-2017, discussing theories, applications and future directions in the field of computational intelligence (CI). ICCI-2017 brought together international researchers presenting innovative work on self-adaptive systems and methods. This volume covers the current state of the field and explores new, open research directions. The book serves as a guide for readers working to develop and validate real-time problems and related applications using computational intelligence. It focuses on systems that deal with raw data intelligently, generate qualitative information that improves decision-making, and behave as smart systems, making it a valuable resource for researchers and professionals alike.
Author: Ana Jesus Lopez-Menendez
Author: Ana Jesus Lopez-Menendez
Author: Pedro Filipe Cunha
Author: Vijay Singh
Author: Vijay P. Singh
Author: Huijuan Cui
Author: Vijay P. Singh
Author: Hongjun Guan 
Author: Manu Joseph
Author: Rajeev R. Raje
Author: Nishchal K. Verma