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Author: Andreas S. Weigend Publisher: Routledge ISBN: 042997227X Category : Social Science Languages : en Pages : 665
Book Description
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.
Author: Andreas S. Weigend Publisher: Routledge ISBN: 042997227X Category : Social Science Languages : en Pages : 665
Book Description
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.
Author: Zura Kakushadze Publisher: Springer ISBN: 3030027929 Category : Business & Economics Languages : en Pages : 480
Book Description
The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.
Author: Rob J Hyndman Publisher: OTexts ISBN: 0987507117 Category : Business & Economics Languages : en Pages : 380
Book Description
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author: CESAR PEREZ LOPEZ Publisher: CESAR PEREZ ISBN: Category : Mathematics Languages : es Pages : 307
Book Description
MATLAB cuenta con la herramienta Deep Learning Toolbox que proporciona algoritmos, funciones y aplicaciones para crear, entrenar, visualizar y simular redes neuronales. Puede realizar clasificación, regresión, agrupamiento, reducción de dimensionalidad, pronóstico de series temporales y modelado y control de sistemas dinámicos. Las redes neuronales dinámicas son adecuadas para la predicción de series temporales. Puede utilizar la app Neural Net Time Series para resolver diferentes tipos de problemas de series temporales. Generalmente es mejor comenzar con la GUI y luego usarla para generar automáticamente scripts de línea de comandos. Antes de utilizar cualquiera de los métodos, el primer paso es definir el problema seleccionando un conjunto de datos. Cada GUI tiene acceso a muchos conjuntos de datos de muestra que puede utilizar para experimentar con la caja de herramientas. Si tiene un problema específico que desea resolver, puede cargar sus propios datos en el espacio de trabajo. Con MATLAB es posible resolver tres tipos diferentes de problemas de series temporales. En el primer tipo de problema de series de tiempo, se busca predecir valores futuros de una serie de tiempo y(t) a partir de valores pasados de esa serie de tiempo y valores pasados de una segunda serie de tiempo x(t). Esta forma de predicción se denomina red autorregresiva no lineal con entrada exógena (externa), o NARX. En el segundo tipo de problema de series temporales, sólo hay una serie involucrada. Los valores futuros de una serie temporal y(t) se predicen sólo a partir de valores pasados de esa serie. Esta forma de predicción se llama autorregresiva no lineal o NAR. El tercer problema de series de tiempo es similar al primer tipo, en el sentido de que están involucradas dos series, una serie de entrada (predictores) x(t) y una serie de salida (respuestas) y(t). Este libro desarrolla los métodos de predicción con series temporales a través de redes neuronales con MATLAB.
Author: Daniel J. Block Publisher: Morgan & Claypool Publishers ISBN: 1598291955 Category : Technology & Engineering Languages : en Pages : 112
Book Description
This monograph describes the Reaction Wheel Pendulum, the newest inverted-pendulum-like device for control education and research. We discuss the history and background of the reaction wheel pendulum and other similar experimental devices. We develop mathematical models of the reaction wheel pendulum in depth, including linear and nonlinear models, and models of the sensors and actuators that are used for feedback control. We treat various aspects of the control problem, from linear control of themotor, to stabilization of the pendulum about an equilibrium configuration using linear control, to the nonlinear control problem of swingup control. We also discuss hybrid and switching control, which is useful for switching between the swingup and balance controllers. We also discuss important practical issues such as friction modeling and friction compensation, quantization of sensor signals, and saturation. This monograph can be used as a supplement for courses in feedback control at the undergraduate level, courses in mechatronics, or courses in linear and nonlinear state space control at the graduate level. It can also be used as a laboratory manual and as a reference for research in nonlinear control.
Author: Osval Antonio Montesinos López Publisher: Springer Nature ISBN: 3030890104 Category : Technology & Engineering Languages : en Pages : 707
Book Description
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author: Kuinam J. Kim Publisher: Springer ISBN: 9811064512 Category : Technology & Engineering Languages : en Pages : 361
Book Description
This is the first volume of proceedings including selected papers from the International Conference on IT Convergence and Security (ICITCS) 2017, presenting a snapshot of the latest issues encountered in this field. It explores how IT convergence and security issues are core to most current research, and industrial and commercial activities. It consists of contributions covering topics such as machine learning & deep learning, communication and signal processing, computer vision and applications, future network technology, artificial intelligence and robotics. ICITCS 2017 is the latest in a series of highly successful International Conferences on IT Convergence and Security, previously held in Prague, Czech Republic(2016), Kuala Lumpur, Malaysia (2015) Beijing, China (2014), Macau, China (2013), Pyeong Chang, Korea (2012), and Suwon, Korea (2011).
Author: Sebastian Raschka Publisher: Packt Publishing Ltd ISBN: 1783555149 Category : Computers Languages : en Pages : 455
Book Description
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Author: Marcelo Naiouf Publisher: Springer ISBN: 9783030848248 Category : Computers Languages : en Pages : 203
Book Description
This book constitutes the revised selected papers of the 9th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2021, held in La Plata, Argentina*, in June 2021. The 12 full papers and 2 short papers presented were carefully reviewed and selected from a total of 37 submissions. The papers are organized in topical sections on parallel and distributed computing; machine and deep learning; big data; web and mobile computing; visualization.. *The conference was held virtually due to the COVID-19 pandemic.
Author: Jorge Garza Ulloa Publisher: Academic Press ISBN: 0128125950 Category : Technology & Engineering Languages : en Pages : 664
Book Description
Applied Biomechatronics Using Mathematical Models provides an appropriate methodology to detect and measure diseases and injuries relating to human kinematics and kinetics. It features mathematical models that, when applied to engineering principles and techniques in the medical field, can be used in assistive devices that work with bodily signals. The use of data in the kinematics and kinetics analysis of the human body, including musculoskeletal kinetics and joints and their relationship to the central nervous system (CNS) is covered, helping users understand how the complex network of symbiotic systems in the skeletal and muscular system work together to allow movement controlled by the CNS. With the use of appropriate electronic sensors at specific areas connected to bio-instruments, we can obtain enough information to create a mathematical model for assistive devices by analyzing the kinematics and kinetics of the human body. The mathematical models developed in this book can provide more effective devices for use in aiding and improving the function of the body in relation to a variety of injuries and diseases. - Focuses on the mathematical modeling of human kinematics and kinetics - Teaches users how to obtain faster results with these mathematical models - Includes a companion website with additional content that presents MATLAB examples
Author: Andreas S. Weigend
Author: Andreas S. Weigend
Author: Zura Kakushadze
Author: Rob J Hyndman
Author: CESAR PEREZ LOPEZ
Author: Daniel J. Block
Author: Osval Antonio Montesinos López
Author: Kuinam J. Kim
Author: Sebastian Raschka
Author: Marcelo Naiouf
Author: Jorge Garza Ulloa