Описание: This book uses real-life datasets from Kaggle to explain basic statistics for machine learning for data segmentation, regression predictions, and forecasts. You`ll focus on variable dependency and autocorrelation to build, test, and use a linear regression prediction model and time series forecasts.
Introduces the latest developments in forecasting in advanced quantitative data analysis
This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable.
Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers.
Presents models that are all classroom tested
Contains real-life data samples
Contains over 350 equation specifications of various time series models
Contains over 200 illustrative examples with special notes and comments
Applicable for time series data of all quantitative studies
Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
Описание: Focusing on the analysis and modeling of thermal systems in engineering, this volume covers the mathematics, data interpretation and decision analysis required for researchers and engineers to scrutinize their methodologies and arrive at robust conclusions.
Описание: This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design.
Автор: Ozaki Tohru Название: Time Series Modeling of Neuroscience Data ISBN: 1420094602 ISBN-13(EAN): 9781420094602 Издательство: Taylor&Francis Рейтинг: Цена: 26030.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.
Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by the Wiener-Kalman-Akaike approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for whitening the temporally dependent time series in the framework of linear/nonlinear state space models. Using as little mathematics as possible, this book explores some of its basic concepts and their derivatives as useful tools for time series analysis. Unique features include:
A statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and more
Methods and applications for Dynamic Causality Analysis developed by Wiener, Granger, and Akaike
A state space modeling method for dynamicization of solutions for the Inverse Problems
A heteroscedastic state space modeling method for dynamic non-stationary signal decomposition for applications to signal detection problems in EEG data analysis
An innovation-based method for the characterization of nonlinear and/or non-Gaussian time series
An innovation-based method for spatial time series modeling for fMRI data analysis
The main point of interest in this book is to show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information can be extracted more efficiently. Of course, time series modeling is valid not only in neuroscience data analysis but also in many other sciences and engineering fields where the statistical inference from the observed time series data plays an important role.
Автор: Pruneau, Claude A. (wayne State University, Michigan) Название: Data analysis techniques for physical scientists ISBN: 1009245007 ISBN-13(EAN): 9781009245005 Издательство: Cambridge Academ Рейтинг: Цена: 5226.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.
Автор: Agresti, Alan, Название: Foundations of statistics for data scientists : ISBN: 0367748452 ISBN-13(EAN): 9780367748456 Издательство: Taylor&Francis Рейтинг: Цена: 14545.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows the elements of statistical science that are highly relevant for students who plan to become data scientists. However, most of the content focuses on the statistical methods and the theory behind them, rather than on data science.
Описание: This is a revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, modeling the effects of intervention events, and process control, among others. In addition to meticulous modifications in content and improvements in style, the new edition incorporates several new topics in an effort to modernize the subject matter. These topics include extensive discussions of multivariate time series, smoothing, likelihood function based on the state space model, autoregressive models, structural component models and deterministic seasonal components, and nonlinear and long memory models.
Описание: This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series.The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing computer science, mathematics, statistics and econometrics.
Автор: Harvey, Andrew C. Название: Forecasting, structural time series models and the kalman filter ISBN: 0521405734 ISBN-13(EAN): 9780521405737 Издательство: Cambridge Academ Рейтинг: Цена: 6018.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose. It is unique in its use of Kalman filtering with econometric and time series modelling.
Описание: Practical Time Series Forecasting: A Hands-On Guide, Third Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the Excel(R) add-in XLMiner(R) to develop effective forecasting solutions that extract business value from time-series data. Featuring improved organization and new material, the Third Edition also includes:
Popular forecasting methods including smoothing algorithms, regression models, and neural networks
A practical approach to evaluating the performance of forecasting solutions
A business-analytics exposition focused on linking time-series forecasting to business goals
Guided cases for integrating the acquired knowledge using real data
End-of-chapter problems to facilitate active learning
A companion site with data sets, learning resources, and instructor materials (solutions to exercises, case studies, and slides)
Globally-available textbook, available in both softcover and Kindle formats
Practical Time Series Forecasting: A Hands-On Guide, Third Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management. For more information, visit forecastingbook.com
Автор: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kul Название: Introduction to Time Series Analysis and Forecasting ISBN: 1118745116 ISBN-13(EAN): 9781118745113 Издательство: Wiley Рейтинг: Цена: 18208.00 р. Наличие на складе: Поставка под заказ.
Описание: Praise for the First Edition " [t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics.
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