Products related to Dynamic:
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Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)
Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called “ADAM” (Augmented Dynamic Adaptive Model).The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems.At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model.ADAM can do all of that in one and the same framework.Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analysts and machine learning experts who work with time series, as well as any researchers working in the area of dynamic models. Key Features:• It covers basics of forecasting,• It discusses ETS and ARIMA models,• It has chapters on extensions of ETS and ARIMA, including how to use explanatory variables and how to capture multiple frequencies,• It discusses intermittent demand and scale models for ETS, ARIMA and regression,• It covers diagnostics tools for ADAM and how to produce forecasts with it,• It does all of that with examples in R.
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The Spatial Grasp Model : Applications and Investigations of Distributed Dynamic Worlds
Born half a century ago, predating the internet and named WAVE in its infantry, the Spatial Grasp Model has been tested on numerous applications: graph and network theory, collective robotics, crisis management, security and defense, social systems, and space-based systems.The Spatial Grasp Model confirms the potential applicability of the developed paradigm, language, and technology for solving much broader classes of problems, especially those related to large and unknown worlds. Presenting the main ideas of the Spatial Grasp paradigm and details of its key Spatial Grasp Language (SGL) – including its philosophy, methodology, syntax, semantics, and interpretation in distributed systems – Sapaty explores extended technological applications of the approach.Allowing us to evaluate large, distributed phenomena by their physical or virtual coverage, The Spatial Grasp Model suggests uses beyond the theoretical, including the examination of hurricanes and forest fires.Investigating group behaviour of ocean animals, discovery of unknown terrain features, and path-findings in large transport networks truly demonstrates the real-world application of SGL. Applicable for high-level formulation of key problems and their solutions in the place of natural languages, The Spatial Grasp Model is crucial reading for researchers across multiple fields faced with developing global.
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Data Analytics & Visualization All-in-One For Dummies
Install data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place.Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling.Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one.You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau.You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level.With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sourcesOrganize and analyze data Use data to tell a story with TableauExpand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data.Get ready to watch as your career in data takes off.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Routing UAVS to Co-Optimize Mission Effectiveness and Network Performance with Dynamic Programming
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Crime Mapping and Spatial Data Analysis using R
Crime mapping and analysis sit at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis.This book brings together relevant knowledge from these fields into a practical, hands-on guide, providing a useful introduction and reference material for topics in crime mapping, the geography of crime, environmental criminology, and crime analysis.It can be used by students, practitioners, and academics alike, whether to develop a university course, to support further training and development, or to hone skills in self-teaching R and crime mapping and spatial data analysis.It is not an advanced statistics textbook, but rather an applied guide and later useful reference books, intended to be read and for readers to practice the learnings from each chapter in sequence.In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data.We then introduce a series of tools to study spatial homogeneity and dependence.A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation.The final chapters introduce the use of basic spatial models, which account for the distribution of crime across space.In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis.
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Spatial Epidemiological Approaches in Disease Mapping and Analysis
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries.Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data.Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.
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Similar search terms for Dynamic:
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What is the difference between static and dynamic routing?
Static routing involves manually configuring the routing table on a network device, specifying the paths to different networks. This method is simple and easy to set up, but it does not adapt to changes in the network topology. Dynamic routing, on the other hand, uses routing protocols to automatically update the routing table based on the current network conditions. This allows for more flexibility and adaptability in handling changes in the network, but it requires more configuration and management.
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How does the dynamic routing protocol Open Shortest Path First (OSPF) work?
Open Shortest Path First (OSPF) is a dynamic routing protocol that uses a link-state algorithm to determine the best path to a destination network. It works by exchanging link-state advertisements (LSAs) between routers to build a complete map of the network. Each router then uses this map to calculate the shortest path to every network in the network. OSPF uses a metric called cost, which is based on the bandwidth of the link, to determine the best path. This allows OSPF to dynamically adapt to changes in the network, such as link failures or new network additions, and quickly converge on a new optimal path.
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What is a dynamic development or a dynamic region?
A dynamic development or region refers to an area that is experiencing rapid growth, change, and innovation. This can be in terms of economic development, technological advancements, population growth, or cultural evolution. Dynamic developments or regions often attract investment, talent, and opportunities, and are characterized by a high level of activity and progress. These areas are often seen as key drivers of overall national or global growth and are typically characterized by their ability to adapt and evolve in response to changing circumstances.
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What is spatial visualization ability?
Spatial visualization ability refers to the capacity to mentally manipulate and comprehend spatial relationships between objects. Individuals with strong spatial visualization skills can easily visualize and understand how objects relate to each other in space, such as rotating or manipulating shapes in their mind. This ability is crucial in various fields such as engineering, architecture, and mathematics, as it allows individuals to solve complex problems and understand spatial concepts more effectively. Improving spatial visualization ability can enhance problem-solving skills and overall cognitive performance.
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Is spatial visualization important for engineers?
Yes, spatial visualization is important for engineers as it allows them to mentally manipulate and understand complex 3D objects and structures. Engineers often need to design and analyze various components and systems, and spatial visualization skills help them to conceptualize and communicate their ideas effectively. Whether it's designing a new product, creating blueprints for a building, or solving complex problems, spatial visualization is a crucial skill that allows engineers to think critically and innovate in their field.
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What are dynamic professions?
Dynamic professions are those that are constantly evolving and adapting to changes in technology, society, and the economy. These professions require individuals to be flexible, open-minded, and willing to learn new skills in order to stay relevant and successful. Examples of dynamic professions include software development, digital marketing, healthcare, and renewable energy. In these fields, professionals must be able to quickly respond to new trends and developments in order to thrive in their careers.
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Can one improve their spatial visualization skills?
Yes, it is possible to improve spatial visualization skills through practice and training. Engaging in activities such as puzzles, building models, and playing spatial reasoning games can help develop these skills. Additionally, practicing mental rotation exercises and regularly challenging oneself with spatial tasks can also contribute to improvement. With consistent effort and dedication, individuals can enhance their spatial visualization abilities over time.
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What are problems with spatial visualization skills?
Some problems with spatial visualization skills include difficulty in understanding and interpreting maps, graphs, and diagrams. Individuals with poor spatial visualization skills may struggle with tasks such as navigating through unfamiliar environments, understanding 3D objects, and mentally rotating objects. This can impact their performance in subjects such as math, science, and engineering, as well as in everyday activities such as driving and assembling furniture. Additionally, poor spatial visualization skills can lead to frustration and decreased confidence in one's abilities.
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