Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.

This IBM® Redbooks® Product Guide is an overview of the main characteristics, features, and technology that are used in IBM FlashSystem® A9000Model 425, with IBM FlashSystem A9000 Software V12.3.2. Software version 12.3.2, with Hyper-Scale Manager version 5.6 or later, introduces support for VLAN tagging and port trunking. IBM FlashSystem A9000 storage system uses the IBM FlashCore® technology to help realize higher capacity and improved response times over disk-based systems and other competing flash and solid-state drive (SSD)-based storage. The extreme performance of IBM FlashCore technology with a grid architecture and comprehensive data reduction creates one powerful solution. Whether you are a service provider who requires highly efficient management or an enterprise that is implementing cloud on a budget, FlashSystem A9000 provides consistent and predictable microsecond response times and the simplicity that you need. The A9000 features always on data reduction and now offers intelligent capacity management for deduplication. As a cloud optimized solution, FlashSystem A9000 suits the requirements of public and private cloud providers who require features, such as inline data deduplication, multi-tenancy, and quality of service. It also uses powerful software-defined storage capabilities from IBM Spectrum™ Accelerate, such as Hyper-Scale technology, VMware, and storage container integration.

This IBM® Redbooks® Product Guide is an overview of the main characteristics, features, and technology that are used in IBM FlashSystem® A9000R Model 415 and Model 425, with IBM FlashSystem A9000R Software V12.3.2. Software version 12.3.2, with Hyper-Scale Manager version 5.6 or later, introduces support for VLAN tagging and port trunking.. IBM FlashSystem A9000R is a grid-scale, all-flash storage platform designed for industry leaders with rapidly growing cloud storage and mixed workload environments to help drive your business into the cognitive era. FlashSystem A9000R provides consistent, extreme performance for dynamic data at scale, integrating the microsecond latency and high availability of IBM FlashCore® technology. The rack-based offering comes integrated with the world class software features that are built with IBM Spectrum™ Accelerate. For example, comprehensive data reduction, including inline pattern removal, data deduplication, and compression, helps lower total cost of ownership (TCO) while the grid architecture and IBM Hyper-Scale framework simplify and automate storage administration. The A9000R features always on data reduction and now offers intelligent capacity management for deduplication. Ready for the cloud and well-suited for large deployments, FlashSystem A9000R delivers predictable high performance and ultra-low latency, even under heavy workloads with full data reduction enabled. As a result, the grid-scale architecture maintains this performance by automatically self-optimizing workloads across all storage resources without manual intervention.

At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the product’s intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career. Who This Book Is For Early-career data scientists, managers of data scientists, and those interested in entering the fieldof data science

Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams

Volume II of this series discusses the technology used to implement a big data analysis capability within a service-oriented organization. It discusses the technical architecture necessary to implement a big data analysis capability, some issues and challenges in big data analysis and utilization that an organization will face, and how to capture value from it. It will help readers understand what technology is required for a basic capability and what the expected benefits are from establishing a big data capability within their organization.

"Principles of Strategic Data Science" is your go-to guide for creating measurable value from data through strategic use of tools and techniques. This book takes you through key theoretical foundations, practical tools, and the managerial perspective necessary to succeed in data science. What this Book will help me do Master the five-phase framework for strategic data science. Learn ways to effectively visualize data information. Explore the role and contributions of a data science manager. Gain clear insights into organizational benefits of data science. Understand the ethical and mathematical boundaries of data analysis. Author(s) Peter Prevos is an accomplished engineer and social scientist with extensive expertise in data science applications. He combines technical insights with social science management practices to design effective data strategies. Known for his clear teaching style, Peter helps professionals integrate theory with practical planning. Who is it for? This book is ideal for data scientists and analysts seeking to deepen their strategic understanding of data science. It's well-suited for intermediate professionals looking to gain insights into data-driven decision making. Readers should have basic programming knowledge in Python or R. Novice managers eager to harness data for organizational goals will also find it valuable.

Here is a practical guide for analyzing and troubleshooting SQL Server performance using wait statistics. Learn to identify precisely why your queries are running slowly. Measure the amount of time consumed by each bottleneck so that you can focus attention on making the largest improvements first. This edition is updated to cover analysis of wait statistics inside Query Store, the CXCONSUMER wait event, and to be current with SQL Server 2019. Whether you are new to wait statistics, or already familiar with them, this book provides a deeper understanding on how wait statistics are generated and what they can mean for your SQL Server instance’s performance. Pro SQL Server 2019 Wait Statistics goes beyond the most common wait types into the more complex and performance-threatening wait types. You’ll learn about per-query wait statistics and session-based wait statistics, and the types of problems they each can help you solve. The different wait types are categorized by their area of impact, including CPU, IO, Lock, and many more. The book presents clear examples to help you gain practical knowledge of why and how specific wait times increase or decrease, and how they impact your SQL Server’s performance. After reading this book you won’t want to be without the valuable information that wait statistics provide regarding where you should be spending your limited tuning time to maximize performance and value to your business. What You'll Learn Identify resource bottlenecks in a running SQL Server instance Locate wait statistics information inside DMVs and Query Store Analyze the root cause of sub-optimal performance Diagnose I/O contention and locking contention Benchmark SQL Server performance Lower the wait time of the most popular wait types Who This Book Is For Database administrators who want to identify and resolve performance bottlenecks, those who want to learn more about how the SQL Server engine accesses and uses resources inside SQL Server, and administrators concerned with achieving—and knowing they have achieved—optimal performance

Applied Supervised Learning with R equips you with the essential knowledge and practical skills to leverage machine learning techniques for solving business problems using R. With this book, you'll gain hands-on experience in implementing various supervised learning models, assessing their performance, and selecting the best-suited method for your objectives. What this Book will help me do Gain expertise in identifying and framing business problems suitable for supervised learning. Acquire skills in data wrangling and visualization using R packages like dplyr and ggplot2. Master techniques for tuning hyperparameters to optimize machine learning models. Understand methods for feature selection and dimensionality reduction to enhance model performance. Learn how to deploy machine learning models to production environments, such as AWS Lambda. Author(s) Karthik Ramasubramanian and Jojo Moolayil are both seasoned data science practitioners and educators who bring a wealth of experience in machine learning and analytics. With a deep understanding of R and its applications in real-world scenarios, they offer practical insights and actionable examples to their readers. Their teaching style focuses on clarity and practical application. Who is it for? This book is ideal for data analysts, data scientists, and data engineers at a beginner to intermediate level who aim to master supervised machine learning with R. Readers should have basic knowledge of statistics, probabilities, and R programming. It is designed for those eager to apply machine learning techniques to real-world problems and improve their decision-making capabilities.

"Geospatial Data Science Quick Start Guide" provides a practical and effective introduction to leveraging geospatial data in data science. In this book, you will learn techniques for analyzing location-based data, building intelligent models, and performing geospatial operations for various applications. What this Book will help me do Understand the principles and techniques for analyzing geospatial data. Set up Python tools to work effectively with location intelligence. Perform advanced spatial operations such as geocoding and proximity analysis. Develop systems such as geofencing and location-based recommendation engines. Obtain actionable insights by visualizing and processing spatial data effectively. Author(s) Abdishakur Hassan and Jayakrishnan Vijayaraghavan are experts in geospatial analysis. With extensive experience in applying data science to location intelligence, they bring a practical and hands-on approach to coding, teaching, and problem-solving. They are passionate about sharing their knowledge through their clear explanations and structured learning paths. Who is it for? This book is ideal for data scientists interested in integrating geospatial analysis into their models and workflows. It is also suitable for GIS developers looking to enhance existing systems with advanced data analysis capabilities. Readers should have experience with Python and a basic understanding of data science concepts. If location-based data intrigues you, this book is your guide.

Immerse yourself in 'Hands-On Exploratory Data Analysis with R,' a comprehensive guide designed to hone your skills in data analysis using the powerful R programming language. This book walks you through all essential aspects of exploratory data analysis, from data collection and cleaning to generating insights with statistical and graphical methods, setting you up for success with any dataset. What this Book will help me do Utilize powerful R packages to accelerate your data analysis workflow. Effectively import, clean, and prepare diverse datasets for analysis. Create informative and visually appealing data visualizations using ggplot2. Generate comprehensive and sharable reports with R Markdown and knitr. Handle multi-factor, optimization, and regression data challenges. Author(s) Radhika Datar and Harish Garg are experienced data analysts and educators specializing in using R for practical data analysis. They have developed this book to share their depth of expertise, offering a detailed yet approachable learning experience. Their combined experience in teaching and applying data analysis in real-world scenarios makes this book an invaluable resource for practitioners. Who is it for? This book is perfect for data enthusiasts looking to strengthen their foundational knowledge in exploratory data analysis. Data analysts, engineers, software developers, and product managers seeking to broaden their skillset in data interpretation and visualization will find this guide extremely beneficial. Whether you're a beginner or already possess basic understanding of data analysis, this book will provide actionable insights to improve your workflow.

Dive into the intricacies of time series analysis and forecasting with R in this comprehensive guide. From foundational concepts to practical implementations, this book equips you with the tools and techniques to analyze, understand, and predict time-dependent data. What this Book will help me do Develop insights by visualizing time-series data and identifying patterns. Master statistical time-series concepts including autocorrelation and moving averages. Learn and implement forecasting models like ARIMA and exponential smoothing. Apply machine learning methodologies for advanced time-series predictions. Work with key R packages for cleaning, manipulating, and analyzing time-series data. Author(s) Rami Krispin is an accomplished statistician and R programmer with extensive experience in data analysis and time-series modeling. His hands-on approach in utilizing R packages and libraries brings clarity to complex time-series concepts. With a passion for teaching and simplifying intricate topics, Rami ensures readers both grasp the theories and apply them effectively. Who is it for? This book is ideal for data analysts, statisticians, and R developers interested in mastering time-series analysis for real-world applications. Designed for readers with a basic understanding of statistics and R programming, it offers a practical approach to learning effective forecasting and data visualization techniques. Professionals aiming to expand their skillset in predictive analytics will find it particularly beneficial.

"Learning Elastic Stack 7.0" introduces you to the tools and techniques of Elastic Stack, covering Elasticsearch, Logstash, Beats, and Kibana. With clear explanations and practical examples, this book helps you grasp the 7.0 version's new features and capabilities, empowering you to build and deploy robust, real-time data processing applications. What this Book will help me do Gain the necessary skills to install and configure Elastic Stack for professional use. Master the data handling capabilities of Elasticsearch for distributed search and analytics. Develop expertise in creating data pipelines with Logstash and other ingestion tools. Learn to utilize Kibana to visualize and interpret complex datasets. Acquire knowledge of deploying Elastic Stack solutions both on-premise and in cloud environments. Author(s) Pranav Shukla and Sharath Kumar M N are experienced software engineers and data professionals with a profound knowledge of databases, distributed systems, and cloud architectures. They specialize in educating developers through structured guidance and proven methodologies related to data handling and visualization. Who is it for? This book is designed for software engineers, data analysts, and technical architects interested in learning the Elastic Stack tools from the ground up. Readers familiar with database concepts but new to Elastic Stack will find this book particularly helpful. Advanced users seeking to understand the updates in Elastic Stack 7.0 are also a complementary audience. If you wish to apply Elastic Stack to real-time data processing and analytics, this book provides a strong foundation.

Mastering SAP ABAP guides you through learning and applying the powerful SAP ABAP programming language. You will start with foundational concepts of programming within SAP environments and progress towards advanced topics such as UI development with SAPUI5 and optimizing ABAP code performance. What this Book will help me do Master the ABAP programming language, from fundamental constructs to advanced techniques. Learn to design and implement efficient and maintainable SAP applications. Gain expertise in creating modern UIs for SAP systems using SAPUI5. Understand performance optimization techniques for SAP ABAP programs. Acquire skills to handle exceptions and perform robust testing in ABAP. Author(s) The authors, Paweł Grzełkowiak, Philipp Deth, Wojciech Ciesielski, and Wojciech Łuźwik, are seasoned SAP technologists with years of practical experience in development and consulting. Their dedication to clarity and usefulness is evident in this book, where they share their collective expertise. Who is it for? This book is for SAP developers, both budding and experienced, who want to increase their efficiency in ABAP programming. Prior exposure to programming concepts and a desire to understand SAP-specific technologies are required prerequisites. Whether you are delving deeper into your career as an SAP developer or are aiming to bring new technical solutions to your organization, this guide is ideal for you.

For decades researchers and programmers have used SAS to analyze, summarize, and report clinical trial data. Now Chris Holland and Jack Shostak have updated their popular Implementing CDISC Using SAS, the first comprehensive book on applying clinical research data and metadata to the Clinical Data Interchange Standards Consortium (CDISC) standards. Implementing CDISC Using SAS: An End-to-End Guide, Revised Second Edition, is an all-inclusive guide on how to implement and analyze the Study Data Tabulation Model (SDTM) and the Analysis Data Model (ADaM) data and prepare clinical trial data for regulatory submission. Updated to reflect the 2017 FDA mandate for adherence to CDISC standards, this new edition covers creating and using metadata, developing conversion specifications, implementing and validating SDTM and ADaM data, determining solutions for legacy data conversions, and preparing data for regulatory submission. The book covers products such as Base SAS, SAS Clinical Data Integration, and the SAS Clinical Standards Toolkit, as well as JMP Clinical. Topics included in this edition include an implementation of the Define-XML 2.0 standard, new SDTM domains, validation with Pinnacle 21 software, event narratives in JMP Clinical, STDM and ADAM metadata spreadsheets, and of course new versions of SAS and JMP software. The second edition was revised to add the latest C-Codes from the most recent release as well as update the make_define macro that accompanies this book in order to add the capability to handle C-Codes. The metadata spreadsheets were updated accordingly. Any manager or user of clinical trial data in this day and age is likely to benefit from knowing how to either put data into a CDISC standard or analyzing and finding data once it is in a CDISC format. If you are one such person--a data manager, clinical and/or statistical programmer, biostatistician, or even a clinician--then this book is for you.

Dive deep into how machine learning is transforming the financial industry with 'Machine Learning for Finance'. This comprehensive guide explores cutting-edge concepts in machine learning while providing practical insights and Python code examples to help readers apply these techniques to real-world financial scenarios. Whether tackling fraud detection, financial forecasting, or sentiment analysis, this book equips you with the understanding and tools needed to excel. What this Book will help me do Understand and implement machine learning techniques for structured data, natural language, images, and text. Learn Python-based tools and libraries such as scikit-learn, Keras, and TensorFlow for financial data analysis. Apply machine learning for tasks like predicting financial trends, detecting fraud, and customer sentiment analysis. Explore advanced topics such as neural networks, generative adversarial networks (GANs), and reinforcement learning. Gain hands-on experience with machine learning debugging, products launch preparation, and addressing bias in data. Author(s) James Le None and Jannes Klaas are experts in machine learning applications in financial technology. Jannes has extensive experience training financial professionals on implementing machine learning strategies in their work and pairs this with a deep academic understanding of the topic. Their dedication to empowering readers to confidently integrate AI and machine learning into financial applications shines through in this user-focused, richly detailed book. Who is it for? This book is tailored for financial professionals, data scientists, and enthusiasts aiming to harness machine learning's potential in finance. Readers should have a foundational understanding of mathematics, statistics, and Python programming. If you work in financial services and are curious about applications ranging from fraud detection to trend forecasting, this resource is for you. It's designed for those looking to advance their skills and make impactful contributions in financial technology.

This volume will assist readers in fitting big data analysis into their service-based organizations. Volume I of this two-volume series focuses on the role of big data in service delivery systems. It discusses the definition and orientation to big data, applications of it in service delivery systems, how to obtain results that can affect/enhance service delivery, and how to build an effective big data organization. This volume will assist readers in fitting big data analysis into their service-based organizations. It will also help readers understand how to improve the use of big data to enhance their service-oriented organizations.

Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding. Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more. Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies JAGS and R codes are provided on an accompanying website to enable practitioners to easily copy them and tailor them to their own applications Practical Applications of Bayesian Reliability is a helpful book for industry practitioners such as reliability engineers, mechanical engineers, electrical engineers, product engineers, system engineers, and materials scientists whose work includes predicting design or product performance.

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. Volume 2 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into four parts: Part 1 examines (in)dependence relationships, innovation in the Nordic countries, dentistry journals, dependence among growth rates of GDP of V4 countries, emissions mitigation, and five-star ratings; Part 2 investigates access to credit for SMEs, gender-based impacts given Southern Europe’s economic crisis, and labor market transition probabilities; Part 3 looks at recruitment at university job-placement offices and the Program for International Student Assessment; and Part 4 examines discriminants, PageRank, and the political spectrum of Germany.