In our era, where the production, processing, and interpretation of information have become more critical than ever, time series analysis is at the heart of scientific research and policy design processes. Many variables, from economic indicators and financial market data to health statistics and production and cost indices, gain meaning within the time dimension; accurate analysis of the past behavior of these variables forms the basis for developing sound predictions for the future. With the widespread adoption of data-driven decision-making, the importance of time series methods is increasing not only in the academic world but also in public and private sector applications.

This work, entitled “Time Series Analysis: Current Methods and Applications I,” aims to address the methodological transformation in the time series literature and the expansion of interdisciplinary application areas from a holistic perspective. The book combines classical econometric approaches with next-generation methods based on machine learning and deep learning, offering both theoretical depth and application diversity. In this respect, the work goes beyond being merely a resource introducing methods; it reveals the multidimensional nature of time series analysis through different data types, modeling strategies, and policy implications.

The chapters in this book focus on significant economic and social issues on both global and national scales.

This book aims to be a comprehensive reference source for academics, graduate students, data scientists, financial analysts, and policymakers working in the field of time series analysis. Shaped by contributions from researchers across different disciplines, this work offers the reader a broad perspective by bringing together both theoretical discussions and application examples. Furthermore, the combined evaluation of classical methods and modern artificial intelligence techniques creates an important research agenda for future studies.

I would like to express my sincere gratitude to all the chapter authors for their scientific contributions and meticulous work in preparing this book. I hope that this book will make a valuable contribution to the time series literature, provide new ideas to researchers, and serve as a guide for practitioners.