A Two Stage Supervised Learning Approach For Electricity Price Forecasting By Leveraging Different Data Sources

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Applied Operations Research and Financial Modelling in Energy

Author : André B. Dorsman
Publisher : Springer Nature
ISBN 13 : 3030849813
Total Pages : 283 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Applied Operations Research and Financial Modelling in Energy by : André B. Dorsman

Download or read book Applied Operations Research and Financial Modelling in Energy written by André B. Dorsman and published by Springer Nature. This book was released on 2021-10-12 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on Applied Operations Research and Financial Modelling in Energy (AORFME) presents several applications of operations research (OR) and financial modelling. The contributions by a group of OR and Finance researchers focus on a variety of energy decisions, presenting a quantitative perspective, and providing policy implications of the proposed or applied methodologies. The content is divided into three main parts: Applied OR I: Optimization Approaches, Applied OR II: Forecasting Approaches and Financial Modelling: Impacts of Energy Policies and Developments in Energy Markets. The book appeals to scholars in economics, finance and operations research, and to practitioners working in the energy sector. This is the eighth volume in a series of books on energy organized by the Centre for Energy and Value Issues (CEVI). For this volume, CEVI collaborated with Hacettepe University’s Energy Markets Research and Application Center. The previous volumes in the series are: Financial Aspects in Energy (2011), Energy Economics and Financial Markets (2012), Perspectives on Energy Risk (2014), Energy Technology and Valuation Issues (2015), Energy and Finance (2016), Energy Economy, Finance and Geostrategy (2018), and Financial Implications of Regulations in the Energy Industry (2020).

Data Analytics in Power Markets

Author : Qixin Chen
Publisher : Springer Nature
ISBN 13 : 9811649758
Total Pages : 292 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Data Analytics in Power Markets by : Qixin Chen

Download or read book Data Analytics in Power Markets written by Qixin Chen and published by Springer Nature. This book was released on 2021-10-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.

Data Science and Applications for Modern Power Systems

Author : Le Xie
Publisher : Springer Nature
ISBN 13 : 303129100X
Total Pages : 446 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Data Science and Applications for Modern Power Systems by : Le Xie

Download or read book Data Science and Applications for Modern Power Systems written by Le Xie and published by Springer Nature. This book was released on 2023-06-20 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid.

Application of Machine Learning and Deep Learning Methods to Power System Problems

Author : Morteza Nazari-Heris
Publisher : Springer Nature
ISBN 13 : 3030776964
Total Pages : 391 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Application of Machine Learning and Deep Learning Methods to Power System Problems by : Morteza Nazari-Heris

Download or read book Application of Machine Learning and Deep Learning Methods to Power System Problems written by Morteza Nazari-Heris and published by Springer Nature. This book was released on 2021-11-21 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

Advances in Internet, Data and Web Technologies

Author : Leonard Barolli
Publisher : Springer Nature
ISBN 13 : 3030397467
Total Pages : 598 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Advances in Internet, Data and Web Technologies by : Leonard Barolli

Download or read book Advances in Internet, Data and Web Technologies written by Leonard Barolli and published by Springer Nature. This book was released on 2020-01-30 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents original contributions on the theories and practices of emerging Internet, data and web technologies and their applicability in businesses, engineering and academia. The Internet has become the most proliferative platform for emerging large-scale computing paradigms. Among them, data and web technologies are two most prominent paradigms, and manifest in a variety of forms such as data centers, cloud computing, mobile cloud, mobile web services and so on. Together, these technologies form a digital ecosystem based on the data cycle, from capturing to processing, analysis and visualization. The investigation of various research and development issues in this digital ecosystem is made all the more important by the ever-increasing needs of real-life applications, which involve storing and processing large amounts of data. As a key feature, the book addresses advances in the life-cycle exploitation of data generated from the digital ecosystem, and data technologies that create value for businesses, moving toward a collective intelligence approach. Given its scope, the book offers a valuable reference guide for researchers, software developers, practitioners and students interested in the field of data and web technologies.

Web, Artificial Intelligence and Network Applications

Author : Leonard Barolli
Publisher : Springer Nature
ISBN 13 : 3030440389
Total Pages : 1487 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Web, Artificial Intelligence and Network Applications by : Leonard Barolli

Download or read book Web, Artificial Intelligence and Network Applications written by Leonard Barolli and published by Springer Nature. This book was released on 2020-03-30 with total page 1487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings book presents the latest research findings, and theoretical and practical perspectives on innovative methods and development techniques related to the emerging areas of Web computing, intelligent systems and Internet computing. The Web has become an important source of information, and techniques and methodologies that extract quality information are of paramount importance for many Web and Internet applications. Data mining and knowledge discovery play a key role in many of today's major Web applications, such as e-commerce and computer security. Moreover, Web services provide a new platform for enabling service-oriented systems. The emergence of large-scale distributed computing paradigms, such as cloud computing and mobile computing systems, has opened many opportunities for collaboration services, which are at the core of any information system. Artificial intelligence (AI) is an area of computer science that builds intelligent systems and algorithms that work and react like humans. AI techniques and computational intelligence are powerful tools for learning, adaptation, reasoning and planning, and they have the potential to become enabling technologies for future intelligent networks. Research in the field of intelligent systems, robotics, neuroscience, artificial intelligence and cognitive sciences is vital for the future development and innovation of Web and Internet applications. Chapter "An Event-Driven Multi Agent System for Scalable Traffic Optimization" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Applications of Nature-Inspired Computing and Optimization Techniques

Author :
Publisher : Elsevier
ISBN 13 : 0323957692
Total Pages : 566 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Applications of Nature-Inspired Computing and Optimization Techniques by :

Download or read book Applications of Nature-Inspired Computing and Optimization Techniques written by and published by Elsevier. This book was released on 2024-04-04 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Computers, Volume 135 highlights advances in the field, with this new volume, Applications of Nature-inspired Computing and Optimization Techniques presenting interesting chapters on a variety of timely topics, including A Brief Introduction to Nature-inspired Computing, Optimization and Applications, Overview of Non-linear Interval Optimization Problems, Solving the Aircraft Landing Problem using the Bee Colony Optimization (BCO) Algorithm, Situation-based Genetic Network Programming to Solve Agent Control Problems, Small Signal Stability Enhancement of Large Interconnected Power System using Grasshopper Optimization Algorithm Tuned Power System Stabilizer, Air Quality Modelling for Smart Cities of India by Nature Inspired AI – A Sustainable Approach, and much more.Other sections cover Genetic Algorithm for the Optimization of Infectiological Parameter Values under Different Nutritional Status, A Novel Influencer Mutation Strategy for Nature-inspired Optimization Algorithms to Solve Electricity Price Forecasting Problem, Recent Trends in Human and Bio Inspired Computing: Use Case Study from Retail Perspective, Domain Knowledge Enriched Summarization of Legal Judgment Documents via Grey Wolf Optimization, and a host of other topics. - Includes algorithm specific studies that cover basic introduction and analysis of key components of algorithms, such as convergence, solution accuracy, computational costs, tuning, and control of parameters - Comprises some of the major applications of different domains - Presents application specific studies, incorporating ways of designing objective functions, solution representation, and constraint handling

Modeling and Stochastic Learning for Forecasting in High Dimensions

Author : Anestis Antoniadis
Publisher :
ISBN 13 : 9783319187334
Total Pages : 339 pages
Book Rating : 4.1/5 (873 download)

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Book Synopsis Modeling and Stochastic Learning for Forecasting in High Dimensions by : Anestis Antoniadis

Download or read book Modeling and Stochastic Learning for Forecasting in High Dimensions written by Anestis Antoniadis and published by . This book was released on 2015 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

The Economics of Electricity Markets

Author : Darryl R. Biggar
Publisher : John Wiley & Sons
ISBN 13 : 1118775724
Total Pages : 493 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis The Economics of Electricity Markets by : Darryl R. Biggar

Download or read book The Economics of Electricity Markets written by Darryl R. Biggar and published by John Wiley & Sons. This book was released on 2014-07-10 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridges the knowledge gap between engineering and economics in a complex and evolving deregulated electricity industry, enabling readers to understand, operate, plan and design a modern power system With an accessible and progressive style written in straight-forward language, this book covers everything an engineer or economist needs to know to understand, operate within, plan and design an effective liberalized electricity industry, thus serving as both a useful teaching text and a valuable reference. The book focuses on principles and theory which are independent of any one market design. It outlines where the theory is not implemented in practice, perhaps due to other over-riding concerns. The book covers the basic modelling of electricity markets, including the impact of uncertainty (an integral part of generation investment decisions and transmission cost-benefit analysis). It draws out the parallels to the Nordpool market (an important point of reference for Europe). Written from the perspective of the policy-maker, the first part provides the introductory background knowledge required. This includes an understanding of basic economics concepts such as supply and demand, monopoly, market power and marginal cost. The second part of the book asks how a set of generation, load, and transmission resources should be efficiently operated, and the third part focuses on the generation investment decision. Part 4 addresses the question of the management of risk and Part 5 discusses the question of market power. Any power system must be operated at all times in a manner which can accommodate the next potential contingency. This demands responses by generators and loads on a very short timeframe. Part 6 of the book addresses the question of dispatch in the very short run, introducing the distinction between preventive and corrective actions and why preventive actions are sometimes required. The seventh part deals with pricing issues that arise under a regionally-priced market, such as the Australian NEM. This section introduces the notion of regions and interconnectors and how to formulate constraints for the correct pricing outcomes (the issue of "constraint orientation"). Part 8 addresses the fundamental and difficult issue of efficient transmission investment, and finally Part 9 covers issues that arise in the retail market. Bridges the gap between engineering and economics in electricity, covering both the economics and engineering knowledge needed to accurately understand, plan and develop the electricity market Comprehensive coverage of all the key topics in the economics of electricity markets Covers the latest research and policy issues as well as description of the fundamental concepts and principles that can be applied across all markets globally Numerous worked examples and end-of-chapter problems Companion website holding solutions to problems set out in the book, also the relevant simulation (GAMS) codes

Machine Learning for Algorithmic Trading

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216786
Total Pages : 822 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen

Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Deep Learning for Time Series Forecasting

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning for Time Series Forecasting by : Jason Brownlee

Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

Intelligent Decision Technologies

Author : Rui Neves-Silva
Publisher : Springer
ISBN 13 : 9783319367668
Total Pages : 0 pages
Book Rating : 4.3/5 (676 download)

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Book Synopsis Intelligent Decision Technologies by : Rui Neves-Silva

Download or read book Intelligent Decision Technologies written by Rui Neves-Silva and published by Springer. This book was released on 2016-10-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the 57 papers accepted for presentation at the Seventh KES International Conference on Intelligent Decision Technologies (KES-IDT 2015), held in Sorrento, Italy, in June 2015. The conference consists of keynote talks, oral and poster presentations, invited sessions and workshops on the applications and theory of intelligent decision systems and related areas. The conference provides an opportunity for the presentation and discussion of interesting new research results, promoting knowledge transfer and the generation of new ideas. The book will be of interest to all those whose work involves the development and application of intelligent decision systems.

Data Science on AWS

Author : Chris Fregly
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492079367
Total Pages : 524 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Data Science on AWS by : Chris Fregly

Download or read book Data Science on AWS written by Chris Fregly and published by "O'Reilly Media, Inc.". This book was released on 2021-04-07 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Electric Power Systems

Author : João P. S. Catalão
Publisher : CRC Press
ISBN 13 : 1439893969
Total Pages : 462 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Electric Power Systems by : João P. S. Catalão

Download or read book Electric Power Systems written by João P. S. Catalão and published by CRC Press. This book was released on 2017-12-19 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling helps readers develop their skills in modeling, simulating, and optimizing electric power systems. Carefully balancing theory and practice, it presents novel, cutting-edge developments in forecasting and scheduling. The focus is on understanding and solving pivotal problems in the management of electric power generation systems. Methods for Coping with Uncertainty and Risk in Electric Power Generation Outlining real-world problems, the book begins with an overview of electric power generation systems. Since the ability to cope with uncertainty and risk is crucial for power generating companies, the second part of the book examines the latest methods and models for self-scheduling, load forecasting, short-term electricity price forecasting, and wind power forecasting. Toward Optimal Coordination between Hydro, Thermal, and Wind Power Using case studies, the third part of the book investigates how to achieve the most favorable use of available energy sources. Chapters in this section discuss price-based scheduling for generating companies, optimal scheduling of a hydro producer, hydro-thermal coordination, unit commitment with wind generators, and optimal optimization of multigeneration systems. Written in a pedagogical style that will appeal to graduate students, the book also expands on research results that are useful for engineers and researchers. It presents the latest techniques in increasingly important areas of power system operations and planning.

Spot Pricing of Electricity

Author : Fred C. Schweppe
Publisher : Springer Science & Business Media
ISBN 13 : 1461316839
Total Pages : 362 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Spot Pricing of Electricity by : Fred C. Schweppe

Download or read book Spot Pricing of Electricity written by Fred C. Schweppe and published by Springer Science & Business Media. This book was released on 2013-03-07 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a need for fundamental changes in the ways society views electric energy. Electric energy must be treated as a commodity which can be bought, sold, and traded, taking into account its time-and space-varying values and costs. This book presents a complete framework for the establishment of such an energy marketplace. The framework is based on the use of spot prices. In general terms: o An hourly spot price (in dollars per kilowatt hour) reflects the operating and capital costs of generating, transmitting and distributing electric energy. It varies each hour and from place to place. o The spot price based energy marketplace involves a variety of utility-customer transactions (ranging from hourly varying prices to long-term, multiple-year contracts), all of which are based in a consistent manner on hourly spot prices. These transactions may include customers selling to, as well as buying from, the utility. The basic theory and practical implementation issues associated with a spot price based energy marketplace have been developed and discussed through a number of different reports, theses, and papers. Each addresses only a part of the total picture, and often with a somewhat different notation and terminology (which has evolved in parallel with our growing experience). This book was xvii xviii Preface written to serve as a single, integrated sourcebook on the theory and imple mentation of a spot price based energy marketplace.

Artificial Intelligence in Healthcare

Author : Adam Bohr
Publisher : Academic Press
ISBN 13 : 0128184396
Total Pages : 385 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Artificial Intelligence in Healthcare by : Adam Bohr

Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Machine Learning for Time Series Forecasting with Python

Author : Francesca Lazzeri
Publisher : John Wiley & Sons
ISBN 13 : 111968238X
Total Pages : 224 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Machine Learning for Time Series Forecasting with Python by : Francesca Lazzeri

Download or read book Machine Learning for Time Series Forecasting with Python written by Francesca Lazzeri and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.