Modeling And Stochastic Learning For Forecasting In High Dimensions
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Modeling And Stochastic Learning For Forecasting In High Dimensions
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Author : Anestis Antoniadis
language : en
Publisher:
Release Date : 2015
Modeling And Stochastic Learning For Forecasting In High Dimensions written by Anestis Antoniadis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for FORecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in industry and in time series, on nonparametric and functional methods, and on on-line machine learning for forecasting, to the latest developments in tools for high dimension and complex data analysis.
Modeling And Stochastic Learning For Forecasting In High Dimensions
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Author : Anestis Antoniadis
language : en
Publisher: Springer
Release Date : 2015-06-04
Modeling And Stochastic Learning For Forecasting In High Dimensions written by Anestis Antoniadis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-04 with Mathematics categories.
The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry. Forecasting and time series prediction have enjoyed considerable attention over the last few decades, fostered by impressive advances in observational capabilities and measurement procedures. On June 5-7, 2013, an international Workshop on Industry Practices for Forecasting was held in Paris, France, organized and supported by the OSIRIS Department of Electricité de France Research and Development Division. In keeping with tradition, both theoretical statistical results and practical contributions on this active field of statistical research and on forecasting issues in a rapidly evolving industrial environment are presented. The volume reflects the broad spectrum of the conference, including 16 articles contributed by specialists in various areas. The material compiled is broad in scope and ranges from new findings on forecasting in industry and in time series, on nonparametric and functional methods and on on-line machine learning for forecasting, to the latest developments in tools for high dimension and complex data analysis.
Introduction To High Dimensional Statistics
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Author : Christophe Giraud
language : en
Publisher: CRC Press
Release Date : 2014-12-17
Introduction To High Dimensional Statistics written by Christophe Giraud and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-17 with Business & Economics categories.
Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians
Statistical Learning Tools For Electricity Load Forecasting
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Author : Anestis Antoniadis
language : en
Publisher: Springer Nature
Release Date : 2024-08-14
Statistical Learning Tools For Electricity Load Forecasting written by Anestis Antoniadis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-14 with Mathematics categories.
This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.
Prediction Techniques For Renewable Energy Generation And Load Demand Forecasting
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Author : Anuradha Tomar
language : en
Publisher: Springer Nature
Release Date : 2023-01-20
Prediction Techniques For Renewable Energy Generation And Load Demand Forecasting written by Anuradha Tomar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Technology & Engineering categories.
This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.
Interpretability For Industry 4 0 Statistical And Machine Learning Approaches
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Author : Antonio Lepore
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Interpretability For Industry 4 0 Statistical And Machine Learning Approaches written by Antonio Lepore and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Mathematics categories.
This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
Entropy Application For Forecasting
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Author : Ana Jesus Lopez-Menendez
language : en
Publisher: MDPI
Release Date : 2020-12-29
Entropy Application For Forecasting written by Ana Jesus Lopez-Menendez and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-29 with Technology & Engineering categories.
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.
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases
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Author : Rosa Meo
language : en
Publisher: Springer Nature
Release Date : 2025-01-01
Machine Learning And Principles And Practice Of Knowledge Discovery In Databases written by Rosa Meo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-01 with Computers categories.
The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.
Novel Mathematics Inspired By Industrial Challenges
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Author : Michael Günther
language : en
Publisher: Springer Nature
Release Date : 2022-03-30
Novel Mathematics Inspired By Industrial Challenges written by Michael Günther and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-30 with Mathematics categories.
This contributed volume convenes a rich selection of works with a focus on innovative mathematical methods with applications in real-world, industrial problems. Studies included in this book are all motivated by a relevant industrial challenge, and demonstrate that mathematics for industry can be extremely rewarding, leading to new mathematical methods and sometimes even to entirely new fields within mathematics. The book is organized into two parts: Computational Sciences and Engineering, and Data Analysis and Finance. In every chapter, readers will find a brief description of why such work fits into this volume; an explanation on which industrial challenges have been instrumental for their inspiration; and which methods have been developed as a result. All these contribute to a greater unity of the text, benefiting not only practitioners and professionals seeking information on novel techniques but also graduate students in applied mathematics, engineering, and related fields.
Renewable Energy Forecasting And Risk Management
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Author : Philippe Drobinski
language : en
Publisher: Springer
Release Date : 2018-12-27
Renewable Energy Forecasting And Risk Management written by Philippe Drobinski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-27 with Mathematics categories.
Gathering selected, revised and extended contributions from the conference ‘Forecasting and Risk Management for Renewable Energy FOREWER’, which took place in Paris in June 2017, this book focuses on the applications of statistics to the risk management and forecasting problems arising in the renewable energy industry. The different contributions explore all aspects of the energy production chain: forecasting and probabilistic modelling of renewable resources, including probabilistic forecasting approaches; modelling and forecasting of wind and solar power production; prediction of electricity demand; optimal operation of microgrids involving renewable production; and finally the effect of renewable production on electricity market prices. Written by experts in statistics, probability, risk management, economics and electrical engineering, this multidisciplinary volume will serve as a reference on renewable energy risk management and at the same time as a source of inspiration for statisticians and probabilists aiming to work on energy-related problems.