دانلود کتاب تحلیل سری‌های زمانی و شبکه‌ها

Time series analysis is a dynamic research area that has fundamental importance for a wide range of scientific fields, in terms of both a fundamental point of view and applications ranging from physics to engineering as well as biomedical and financial applications. Over the last few decades, many computational methods have been applied and invented to deal with time series-related problems. Beyond conventional time series methodologies, this book introduces the analysis of time series through the perspective of complex networks. By transforming time series data into network, researchers can extract topological features that reveal underlying patterns. But what is extremely interesting in this book is that we also provide detailed guidelines about the software with examples in MATLAB so that anyone can run the corresponding routines and apply them to the data provided or their personal data of research. Moreover, the readers can modify the routines appropriately in order to add more characteristics to the output. This fact is based on the basic idea of learning by trying. The aim of this book is to provide basic knowledge of time series, introduce some statistical tools useful for analyzing these series, and gain experience in applying various linear, nonlinear, and advanced methodologies. We begin with the basic concepts of asset returns and a brief introduction to the processes to be discussed throughout the book. The structure of the book is divided into two main parts. Part I: Linear & Non- Linear Analysis introduces the reader to fundamental statistical tools and methods used in traditional time series analysis. It begins with Chap. 1, which focuses on basic statistical analysis, including descriptive measures, distributions, and hypothesis testing for time series data. Chapter 2 delves into the temporal characteristics of time series, exploring properties such as stationarity, autocorrelation, and trends. Following that, Chap. 3 addresses nonlinear dynamics, including phase space reconstruction, chaos indicators, and methods for detecting complex, nonlinear behavior in time-dependent data. Part II: Complex Network Analysis presents a more advanced perspective, applying network science techniques to the study of time series. In Chap. 4, readers are introduced to complex network representations of time series, such as visibility graphs, and how these can be used to analyze structure, connectivity, and patterns in the data. Finally, Chap. 5 includes extended case studies that combine both traditional and network-based methods, showcasing their application to real-world datasets across different scientific disciplines. This structure allows the reader to progress from foundational concepts to more sophisticated analytical techniques, building a comprehensive understanding of time series and their underlying dynamics. The book can be employed in undergraduate courses as well as in graduate courses, particularly those aiming to provide a fast and practical guide for performing analysis of time series for research purposes in disciplines such as physics, materials science, engineering, and finance, to mention a few areas.