Multivariate Time Series Analysis for Local Forecasting
Author: Geoffrey Maurice Hyman
Publisher:
Published: 1980*
Total Pages: 0
ISBN-13:
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Multivariate Time Series Analysis for Local Forecasting
Author: Geoffrey M. Hyman
Publisher:
Published: 1980
Total Pages: 32
ISBN-13:
DOWNLOAD EBOOKMultivariate Time Series Analysis and Applications
Author: William W. S. Wei
Publisher: John Wiley & Sons
Published: 2019-03-18
Total Pages: 536
ISBN-13: 1119502853
DOWNLOAD EBOOKAn essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.
Multivariate Time Series Analysis
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
Published: 2013-11-11
Total Pages: 414
ISBN-13: 1118617754
DOWNLOAD EBOOKAn accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
The Application of Multivariate Time Series Analysis to Local Area Employment and Demographic Forecasting
Author: Geoffrey M. Hyman
Publisher:
Published: 1980
Total Pages: 23
ISBN-13:
DOWNLOAD EBOOKNew Introduction to Multiple Time Series Analysis
Author: Helmut Lütkepohl
Publisher: Springer Science & Business Media
Published: 2007-07-26
Total Pages: 792
ISBN-13: 9783540262398
DOWNLOAD EBOOKThis is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.
Introduction to Time Series Forecasting With Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Published: 2017-02-16
Total Pages: 359
ISBN-13:
DOWNLOAD EBOOKTime series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.
MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS
Author: Perez M.
Publisher: Createspace Independent Publishing Platform
Published: 2016-06-24
Total Pages: 176
ISBN-13: 9781534868076
DOWNLOAD EBOOKThis book focuses on Multivariate Time Series Models. The most important issues are the following: Vector Autoregressive Models Introduction to Vector Autoregressive (VAR) Models Data Structures Model Specification Structures VAR Model Estimation VAR Model Forecasting, Simulation, and Analysis VAR Model Case Study Cointegration and Error Correction Introduction to Cointegration Analysis Identifying Single Cointegrating Relations Identifying Multiple Cointegrating Relations Testing Cointegrating Vectors and Adjustment Speeds
Time Series
Author: Raquel Prado
Publisher: CRC Press
Published: 2021-07-27
Total Pages: 473
ISBN-13: 1498747043
DOWNLOAD EBOOK• Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.
Deep Learning for Time Series Forecasting
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Published: 2018-08-30
Total Pages: 572
ISBN-13:
DOWNLOAD EBOOKDeep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
