Multivariate Time Series Analysis for Local Forecasting
Author : Geoffrey Maurice Hyman
Publisher :
Page : 0 pages
File Size : 35,83 MB
Release : 1980*
Category : Time-series analysis
ISBN :
Author : Geoffrey Maurice Hyman
Publisher :
Page : 0 pages
File Size : 35,83 MB
Release : 1980*
Category : Time-series analysis
ISBN :
Author : Geoffrey M. Hyman
Publisher :
Page : 32 pages
File Size : 23,82 MB
Release : 1980
Category : Economic forecasting
ISBN :
Author : William W. S. Wei
Publisher : John Wiley & Sons
Page : 536 pages
File Size : 20,70 MB
Release : 2019-03-18
Category : Mathematics
ISBN : 1119502853
An 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.
Author : Ruey S. Tsay
Publisher : John Wiley & Sons
Page : 414 pages
File Size : 44,53 MB
Release : 2013-11-11
Category : Mathematics
ISBN : 1118617754
An 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.
Author : Geoffrey M. Hyman
Publisher :
Page : 23 pages
File Size : 30,49 MB
Release : 1980
Category : Economic forecasting
ISBN :
Author : Helmut Lütkepohl
Publisher : Springer Science & Business Media
Page : 792 pages
File Size : 32,22 MB
Release : 2007-07-26
Category : Business & Economics
ISBN : 9783540262398
This 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.
Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 359 pages
File Size : 29,14 MB
Release : 2017-02-16
Category : Mathematics
ISBN :
Time 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.
Author : Perez M.
Publisher : Createspace Independent Publishing Platform
Page : 176 pages
File Size : 34,2 MB
Release : 2016-06-24
Category :
ISBN : 9781534868076
This 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
Author : Raquel Prado
Publisher : CRC Press
Page : 473 pages
File Size : 46,61 MB
Release : 2021-07-27
Category : Mathematics
ISBN : 1498747043
• 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.
Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 572 pages
File Size : 42,51 MB
Release : 2018-08-30
Category : Computers
ISBN :
Deep 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.