Theory and Applications of Time Series Analysis and Forecasting

This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics,statistics and econometrics.

Presents the latest developments in time series analysis and forecasting Addresses modern theoretical aspects and as well numerous real-world applications Encourages interdisciplinary collaboration between statisticians, econometricians and computer scientists

Auteur
Olga Valenzuela is an Associate Professor at the Department of Applied Mathematics, University of Granada, Spain, where she received her Ph.D. in 2003. She has worked as an invited researcher at the Department of Statistics, University of Jaen, Spain, and at the Department of Computer and Information Science, University of Genova, Italy. Her research interests include optimization theory and applications, statistical analysis, fuzzy systems, neural networks, time series forecasting using linear and non-linear methods, evolutionary computation and bioinformatics. She has published more than 65 papers listed in the Web of Science.

Fernando Rojas is an Associate Professor at the University of Granada, Spain, where he received his Ph.D. in 2004. His research focuses on signal processing, artificial intelligence techniques for optimization, including evolutionary computation, fuzzy logic, neural networks etc., and the study of computer architectures for parallel processing in complex problems, such as time series prediction. He has published over 25 articles in JCR-indexed journals. A former coordinator of the Master's Degree in Computer and Network Engineering at the University of Granada, he has been the secretary of the Master's Degree in Data Science and Computer Engineering since 2014, and the secretary of the Department of Architecture and Computer Technology at the University of Granada since 2018.

Luis Javier Herrera is an Associate Professor at the University of Granada, Spain, where he received his Ph.D. in 2006. His research focuses on machine learning techniques (fuzzy logic, deep learning, genetic algorithms, etc.), and on their optimization and application over a wide range of scientific problems related to classification, approximation and time series prediction, sometimes requiring high-performance computing systems. These applications include relevant problems in several fields such as biomedicine, bioinformatics, biochemistry, physics, optics, etc. He has published more than 40 papers in JCR-indexed journals. Over the last several years, he has (co-)led a number of research projects backed by national and regional funding entities.

Héctor Pomares has been a Full Professor at the University of Granada, Spain, since 2001. He has published more than 50 articles in JCR-indexed journals and contributed over 150 papers to international conferences. He has led or participated in 15 national projects, one independent R&D Excellence project and 13 contracts for innovative research through the University of Granada Foundation Company and the Office of Transfer of Research Results. He has been a visiting researcher at numerous prestigious research centers outside Spain. He is currently a member of the editorial board of the Journal of Applied Mathematics (JCR-indexed) and the coordinator of the Master's Degree in Computer & Network Engineering at the University of Granada.

Ignacio Rojas is a Full Professor at the Department of Computer Architecture and Computer Technology, University of Granada, Spain. His research focuses on the study of complex multidimensional systems using intelligent systems, supported by high-performance computing platforms, and their applications in various fields, including bioinformatics, biomedicine, and time series prediction. Throughout his research career, he has served as a principal investigator or otherwise participated in more than 24 research projects obtained in competitive tenders, including projects for the European Union, the I+D+I Spanish National Government and the Unit of Excellence of the Ministry of Innovation, Science and Enterprise Junta de Andalucía. He has published more than 270 contributions listed in the Web of Science, including more than 120 articles in JCR-indexed journals. He has actively participated in more than 125 international conferences, supervised 25 doctoral theses, and organized various international conferences, workshops and special sessions.

Contenu

• Part I Theoretical Aspects of Time Series. - An Improved Forecasting and Detection of Structural Breaks in Time Series Using Fuzzy Techniques. - Anomaly Detection Algorithm Using a Hybrid Modelling Approach for Energy Consumption Time Series. - Unit Root Test Combination via Random Forests. - Probabilistic Forecasting of Seasonal Time Series. - Nonstatistical Methods for Analysis, Forecasting, and Mining Time Series. - PMF Forecasting for Count Processes: A Comprehensive Performance Analysis. - A Novel First-Order Autoregressive Moving Average Model to Analyze Discrete-Time Series Irregularly Observed. - Part II Econometric and Forecasting. - Using Natural Language Processing to Measure COVID-19-Induced Economic Policy Uncertainty for Canada and the USA. - Asymptotic Expansions for Market Risk Assessment: Evidence in Energy and Commodity Indices. - Predicting Housing Prices for Spanish Regions. - Optimal Combination Forecast for Bitcoin Dollars Time Series. - The Impact of the Hungarian Retail Debt Program. - Predicting the Exchange Rate Path: The Importance of Using Up-to-Date Observations in the Forecasts. - Part III Time Series Prediction Applications. - Development of Algorithm for Forecasting System Software. - Forecasting High-Frequency Electricity Demand in Uruguay. - Day-Ahead Electricity Load Prediction Based on Calendar Features and Temporal Convolutional Networks. - Network Security Situation Awareness Forecasting Based on Neural Networks. - Part IV Advanced Applications in Time Series Analysis.
  • Modeling Covid-19 Contagion Dynamics: Time-Series Analysis Across Different Countries and Subperiods. - Diffusion of Renewable Energy for Electricity: An Analysis for Leading Countries. - The State and Perspectives of Employment in the Water Transport System of the Republic of Croatia. - Reversed STIRPAT Modeling: The Role of CO2 Emissions, Population, and Technology for a Growing Affluence.