Forecasting And Analytics With The Augmented Dynamic Adaptive Model Adam
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Forecasting And Analytics With The Augmented Dynamic Adaptive Model Adam
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Author : Ivan Svetunkov
language : en
Publisher:
Release Date : 2024
Forecasting And Analytics With The Augmented Dynamic Adaptive Model Adam written by Ivan Svetunkov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Adaptive sampling (Statistics) categories.
"Forecasting and Analytics with 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"--
Forecasting And Analytics With The Augmented Dynamic Adaptive Model Adam
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Author : Ivan Svetunkov
language : en
Publisher: CRC Press
Release Date : 2023-11-17
Forecasting And Analytics With The Augmented Dynamic Adaptive Model Adam written by Ivan Svetunkov and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-17 with Mathematics categories.
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.
Complex Valued Econometrics With Examples In R
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Author : Sergey Svetunkov
language : en
Publisher: Springer Nature
Release Date : 2024-07-25
Complex Valued Econometrics With Examples In R written by Sergey Svetunkov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Business & Economics categories.
This book explores the application of complex variables to econometric modeling. Providing a thorough introduction to the theory of complex numbers, it extends these concepts to develop complex-valued models that enhance the accuracy and depth of economic forecasting and data analysis. From simple to multiple complex linear regression, the monograph discusses model formulation, estimation techniques, and correlation analysis, supported by examples in R. This comprehensive guide is a useful resource for students, researchers, and practitioners aiming to apply advanced mathematical techniques to tackle complex real-life problems, making it a useful tool for enhancing predictive analytics in business, economics, and finance.
Forecasting Economic Time Series
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Author : Michael Clements
language : en
Publisher: Cambridge University Press
Release Date : 1998-10-08
Forecasting Economic Time Series written by Michael Clements and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-10-08 with Business & Economics categories.
This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.
Data Science And Intelligent Applications
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Author : Ketan Kotecha
language : en
Publisher: Springer Nature
Release Date : 2020-06-17
Data Science And Intelligent Applications written by Ketan Kotecha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-17 with Technology & Engineering categories.
This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.
Analysis Of Integrated And Cointegrated Time Series With R
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Author : Bernhard Pfaff
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-03
Analysis Of Integrated And Cointegrated Time Series With R written by Bernhard Pfaff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-03 with Business & Economics categories.
This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.
Artificial Intelligence And Machine Learning Applications For Sustainable Development
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Author : A. J. Singh
language : en
Publisher: CRC Press
Release Date : 2025-01-28
Artificial Intelligence And Machine Learning Applications For Sustainable Development written by A. J. Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Technology & Engineering categories.
The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems. This book: Discusses artificial intelligence-based techniques, namely, neural networks, fuzzy expert systems, optimization techniques, and operational research Showcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applications Illustrates strategies for sustainable development using artificial intelligence and machine learning applications Presents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilization Highlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sources It is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical, electronics and communications, energy, and environmental engineering.
Forecasting With Exponential Smoothing
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Author : Rob Hyndman
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-06-19
Forecasting With Exponential Smoothing written by Rob Hyndman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-19 with Mathematics categories.
Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.
Artificial Intelligence In Healthcare
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Author : Adam Bohr
language : en
Publisher: Academic Press
Release Date : 2020-06-21
Artificial Intelligence In Healthcare written by Adam Bohr and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-21 with Computers categories.
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
Advanced Forecasting With Python
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Author : Joos Korstanje
language : en
Publisher: Apress
Release Date : 2021-07-03
Advanced Forecasting With Python written by Joos Korstanje and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-03 with Computers categories.
Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.