Introduction to Time Series Modeling with Applications in R: with Applications in R

The second edition of Introduction to Time Series Modeling with Applications in R is a valuable book for beginners and experts alike,as it covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. It employs the state-space model as a generic tool for time series modeling and presents the Kalman filter,the non-Gaussian filter,and the particle filter as convenient tools for recursive estimation. It also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection,including the least squares method,the maximum likelihood method,recursive estimation for state-space models,and model selection by AIC.

Format: Paperback / softback
Length: 324 pages
Publication date: 01 August 2022
Publisher: Taylor & Francis Ltd

Introduction to Time Series Modeling with Applications in R, Second Edition is a comprehensive guide to understanding, predicting, and mastering time series data. Written by renowned experts in the field, this book offers a detailed introduction to various stationary and nonstationary time series models, as well as tools for estimating and utilizing them.

The book's main goal is to enable readers to build their own models to understand, predict, and master time series data. To achieve this, the second edition provides a step-by-step approach to time series modeling, covering topics such as state-space models, recursive filtering, smoothing, and parameter estimation.

One of the key features of this book is its use of the state-space model as a generic tool for time series modeling. The state-space model is a powerful framework that allows for the representation of time series data as a combination of a deterministic state and a stochastic process. This approach provides a flexible and efficient way to model a wide range of time series phenomena, including seasonal variations, trend changes, and abrupt shifts.

The book also presents the Kalman filter, the non-Gaussian filter, and the particle filter as convenient tools for recursive estimation for state-space models. These filters are widely used in time series analysis and offer various advantages, such as robustness to noise and uncertainty, and the ability to handle complex systems with multiple variables.

In addition to the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as t. These models are particularly useful for capturing complex patterns in time series data, such as seasonal variations, trend changes, and abrupt shifts.

The book employs a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models, and model selection by AIC. This approach ensures that readers can choose the most appropriate method for their specific time series data and modeling needs.

Throughout the book, numerous real-world examples and case studies are provided to illustrate the practical applications of time series modeling. These examples cover a wide range of industries, including finance, weather forecasting, and healthcare, and demonstrate the importance of time series analysis in decision-making and problem-solving.

In conclusion, Introduction to Time Series Modeling with Applications in R, Second Edition is a valuable resource for anyone interested in understanding, predicting, and mastering time series data. With its comprehensive coverage of stationary and nonstationary time series models, as well as practical tools for estimation and utilization, this book provides readers with the skills and knowledge needed to build their own models and make informed decisions based on time series data. Whether you are a researcher, analyst, or practitioner, this book is an essential tool for your time series analysis toolkit.

Dimension: 234 x 156 (mm)
ISBN-13: 9780367494247
Edition number: 2 ed