New Advances and Methodologies in the Field of Time Series and Forecasting—ITISE-2025 ^†

1. Introduction

ITISE-2025 (11th International conference on Time Series and Forecasting) seeks to provide a forum for scientists, engineers, educators, and students to discuss the latest ideas and realizations in the foundations, theory, and models of and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc., in the field of time series analysis and forecasting.

Time series forecasting and econometrics are fundamental tools in economic analysis and decision-making. Time series prediction enables organizations and policymakers to anticipate future trends based on historical data, supporting strategic planning, risk management, and resource allocation.

The aims of ITISE-2025 are to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees, and therefore, ITISE-2025 solicits high-quality original research papers on any aspect related to time series analysis and forecasting.

The conference organizers are Prof. Olga Valenzuela, Prof. Fernando Rojas, Prof. Luis Javier Herrera, Prof. Hector Pomares, and Prof. Ignacio Rojas, all from the University of Granada, Spain. The web page is: https://itise.ugr.es/, where more information about the Scientific Committee can be found.

2. Main Topics of ITISE

As is well known, ITISE aims to provide a friendly discussion forum for scientists, engineers, educators, and students to discuss the latest ideas and achievements in the fundamentals, theory, models, and applications in the field of time series analysis and forecasting. More specifically, the main topics of ITISE are as follows:

1.

Time series analysis and forecasting

• Nonparametric and functional methods.
• Vector processes.
• Probabilistic approaches to modelling macroeconomic uncertainties.
• Uncertainties in forecasting processes.
• Nonstationarity.
• Forecasting with Many Models. Model integration.
• Forecasting theory and adjustment.
• Ensemble forecasting.
• Forecasting performance evaluation.
• Interval forecasting.
• Data preprocessing methods: Data decomposition, seasonal adjustment, singular.
• Spectrum analysis, detrending methods, etc.

2.

Econometrics and forecasting

• Econometric models.
• Economic and econometric forecasting.
• Real macroeconomic monitoring and forecasting.
• Advanced econometric methods.

3.

Advanced methods and on-line learning in time series

• Adaptivity for stochastic models.
• On-line machine learning for forecasting.
• Aggregation of predictors.
• Hierarchical forecasting.
• Forecasting with computational intelligence.
• Time series analysis with computational intelligence.
• Integration of system dynamics and forecasting models.

4.

High dimension and complex/big data

• Local vs. global forecasts.
• Dimension reduction techniques.
• Multiscaling.
• Forecasting Complex/Big data.

5.

Forecasting in real problems

• Health forecasting.
• Atmospheric science forecasting.
• Telecommunication forecasting.
• Hydrological forecasting.
• Traffic forecasting.
• Tourism forecasting.
• Marketing forecasting.
• Modelling and forecasting in power markets.
• Energy forecasting.
• Climate forecasting.
• Financial forecasting and risk analysis.
• Forecasting electricity load and prices.
• Forecasting and planning systems.

3. Special Session in ITISE-2025

At ITISE-2025, a number of special sessions took place, aimed at enriching the main conference programme with innovative and emerging subjects of interest to the academic and professional community. These sessions were particularly focused on interdisciplinary themes and forward-looking topics. Proposals that highlighted cross-cutting areas were strongly encouraged, and for ITISE-2025, the following sessions were accepted:

SS1. Forecasting in Space and Time: Methods for Quantifying Geospatial and Time-Series Effects.

Geospatial and time-series analysis share a common mathematical mission: Extracting effects varying over space, time, or some other dimension not captured by other aspects of the data. Although geospatial and time-series analysis are usually evaluated in isolation from one another, these broad approaches to econometrics have devised at least three distinct approaches to their shared task:

• Data engineering, or the creation of new variables and their addition to the unconditional design matrix that would capture geospatial or intertemporal effects. This approach is typified by fixed entity and/or time effects in mixed linear models.
• Two-stage least squares (2SLS) regression of residuals according to distance is a common approach in geospatial econometrics. It can be applied to time series, but rarely is.
• Iterative local regression characterizes many exercises in time-series forecasting. It can be applied to geospatial analysis, but rarely is.

Notwithstanding the convention of including the phrase “least squares” in 2SLS, none of these approaches is confined to ordinary least squares. Generalized linear methods, such as those incorporating the l1 penalty, and machine learning methods ranging from Gaussian process regression to decision-tree and boosting ensembles operate alongside baseline OLS regressions. Methodological heterodoxy is regarded as an affirmative strength in this overview of forecasting methods across time and space.

The presentations in this special session will cover the conceptual and methodological overlap between geospatial econometrics and time-series forecasting. The mathematics of all three analytical strategies—data engineering, two-stage least squares correction of the error term, and iterative local regression—can be generalized to other tasks, not only in economics, but also to other social and natural sciences.

Organizers: Prof. Dr. James Ming Chen is Justin Smith Morrill Chair in Law and Professor of Law at Michigan State University. Professor Chen holds a law degree from Harvard University and a master’s degree in data science from Northwestern University. He is a member of the American Law Institute and a senior fellow of the Administrative Conference of the United States. Professor Chen’s scholarship covers law, economics, and machine learning.

SS2. Functional time series analysis and application.

This special session is set to provide a comprehensive overview of the latest advancements and methodologies in the analysis of functional data observed over time. Functional time series analysis extends beyond traditional time series by considering data that are functions or curves at each time point, enabling a more nuanced understanding of complex dynamics in various fields.

Participants will delve into the theoretical underpinnings of functional data analysis (FDA) and discuss how these methods can be applied to capture the intrinsic variability in datasets.

Moreover, the session will highlight the practical applications of functional time series analysis across a diverse range of disciplines such as finance, meteorology, environmental science, and health sciences. Presentations and discussions will focus on how these techniques are used to model and forecast complex phenomena, such as intraday price curves in financial markets, daily temperature profiles, pollution levels over time, and growth curves in biostatistics. The goal is to foster an interdisciplinary exchange of ideas, promoting the integration of FDA techniques into mainstream time series analysis and encouraging collaboration between statisticians, data scientists, and domain experts. By exploring both the methodological innovations and their applications, this session aims to provide attendees with a deeper understanding of the potential of functional time series analysis to address contemporary challenges in data analysis and prediction.

Organizer: Prof. Nengxiang Ling, Hefei University of Technology, Hefei, China.

SS3. Advances in Hydroinformatics and Geo/Statistics for modelling and risk assessment of water systems.

Guaranteeing security to cope with water risks (mainly floods and droughts) is essential not only to preserve and maintain the environment, but also to protect socio-economic activity. However, population growth and its associated economic needs, as well as the evident modifications of the hydrological cycle patterns arising from increasing climate variability are seriously compromising the achievement of these objectives. In the face of this global challenge, new approaches and advances in the fields of Hydroinformatics and Geo/Statistics can help to improve the assessment of these increasingly systemic natural hazards.

This section focuses on improving risk assessment in water systems, covering a wide range of topics, including: drought characterization, new developments based on EPIC responses, innovative methodologies for time series analysis, advances in modelling of water systems, spatio-temporal hydrological-hydraulic analysis of risk induced by flash and extreme events, advances in forest restoration and/or development of Nature-based Solutions to mitigate the effects of floods.

Special Issue in Water. Guest Editors Profs. Dr. Santiago Zazo, Prof. Dr. José-Luis Molina, Dr. Carmen Patino-Alonso, and Dr. Fernando Espejo

Special Issue (SI): “Advances in Hydroinformatics and Geo/Statistics for Modelling and Risk Assessment of Water Systems”, in MDPI journal Water.

Organizer: Prof. Santiago Zazo del Dedo, IGA Research Group—University of Salamanca, Spain. Dr. José-Luis Molina  (Full Professor—Hydraulic Engineering Area), Dra. Carmen Patino (Associate Profesor—Statistics and Operations Research Area), and Dr. Fernando Espejo (Associate Professor - Hydraulic Engineering Area).

SS4. Forecasting In High Dimension And Complex/Big Data.

This session is designed to address the growing challenges and opportunities presented via the analysis of high-dimensional and complex datasets. In an era where big data has become ubiquitous across various industries, from finance and marketing to healthcare and environmental studies, the need for sophisticated forecasting methods that can effectively handle the volume, variety, and velocity of such data is more critical than ever. This session aims to explore the latest developments in statistical and machine learning techniques tailored for high-dimensional time series forecasting. Topics will include dimensionality reduction, regularization methods, and advances in computational algorithms that enable the extraction of meaningful patterns and predictions from large datasets.

In addition, this session will provide a platform to discuss the application of these advanced forecasting methods in real-world scenarios and illustrate how they can uncover insights and support decision making in complex systems. Attendees will hear from experts who have successfully overcome the challenges of working with big data, including dealing with sparsity and dependency structures in high-dimensional spaces, integrating heterogeneous data sources, and ensuring the interpretability and robustness of models. The discussions emphasise innovative approaches such as the use of deep learning and ensemble models to improve prediction accuracy and reliability. Through a combination of theoretical insights and practical case studies, this session aims to equip participants with the knowledge and tools to harness the power of big data for forecasting in their respective fields, paving the way for breakthrough advances in time series analysis.

Organizers: Prof. Fernando Rojas and Prof. Luis Javier Herrera, University of Granada, Spain.

SS5. Computational Intelligence Methods For Time Series.

Within the field of science and engineering, it is very common to have data arranged in the form of time series data which must be subsequently analysed, modeled, and classified with the eventual goal of predicting future values. The literature shows that all these tasks related to time series can be undertaken using computational intelligence methods. In fact, new and further computational intelligence approaches, as well as their efficiency and their comparison to statistical methods and other fact-checked computational intelligence methods, represent a significant topic in academic and professional projects and works. Therefore, this special session aims at presenting to our research community high-quality and state-of-the-art computational intelligence (and statistical)-related works, applied to time series data and their tasks: analysis, forecasting, classification, and clustering. Furthermore, the experts can, from the starting point that the works shown provide, discuss different solutions and research issues for these topics.

In the rapidly evolving field of time series analysis, the integration of computational intelligence (CI) methods represents a frontier of innovation and exploration. The special session on “Computational Intelligence Methods for Time Series” will address the synergies between AI techniques and time series analysis, focusing on how artificial intelligence, machine learning, and evolutionary algorithms can improve predictive modelling and analysis. This convergence aims to tackle complex and dynamic time series data by utilising AI’s adaptability, learning ability and robustness to noise and uncertainty.

This session is not only about presenting novel AI methods, but also about demonstrating their practical effectiveness in various areas such as finance, healthcare, environmental monitoring, and energy forecasting. The focus will be on the use of neural networks, fuzzy systems, genetic algorithms and hybrid models to extract patterns, make predictions and uncover hidden structures in time series data. The focus is on overcoming challenges such as non-linearity, high dimensionality, and prediction of rare events. Through a combination of technical presentations, case studies, and interactive discussions, participants will learn how AI methods can be used creatively to advance the field of time series analysis and provide solutions that are not only computationally efficient, but also interpretable and scalable.

Organizers: Prof. Hector Pomares, University of Granada, Spain.

SS6. Advanced Econometric Modelling.

The rapid evolution of econometric techniques has significantly enhanced the ability of researchers and practitioners to model and forecast complex economic phenomena. In recent years, the intersection of advanced econometric modelling and time series forecasting has led to groundbreaking developments that improve the precision, robustness, and interpretability of predictive models. This special session aims to bring together leading experts to discuss state-of-the-art methodologies, novel theoretical contributions, and empirical applications that push the frontiers of time series econometrics.

Organizers: Prof. Chen, Faculty of Economics, Chiang Mai University.

SS7. Time Series Analysis With Computational Intelligence In Energy Forecasting.

Energy consumption forecasting is an operation of predicting the future energy consumption of electricity systems based on past or historical data, which has an increasing impact on society in order to have an accurate forecast and prediction of electricity demand and, for example, to avoid the risk of temporary blackouts or a decrease in power quality. In several countries, these blackouts are a common system failure. This is because these sources (solar power plants, wind power plants) depend on weather conditions, which are stochastic by nature.

Electricity demand forecasting is becoming increasingly important. Correct forecasting makes it possible to plan and expand power sector facilities. Accurate forecasts can save operating and maintenance costs, increase the reliability of the energy supply and delivery system and correct future development decisions.

The increasing share of renewable energy sources in energy production is a rapidly growing field of research, innovation and transfer in recent years.

Surpluses and deficits in energy production negatively affect the operation of electricity grids. For a well-functioning energy system, it is essential to adapt energy production to current demand.

In the daily operation of conventional power plants, adjusting and determining energy production according to demand is a task that can be achieved with accurate forecasting systems. This problem and adjustment, however, becomes more complicated in power plants based on renewable sources. This requires accurate forecasting of energy production from renewable sources, especially when the share of such plants is significant in relation to other sources of electricity. At present, it is necessary to develop methods for forecasting electricity production from wind or solar power plants, or renewable energy in general, considering many exogenous factors and, of course, weather conditions.

Existing approaches to forecasting models can be classified into the following four categories: physical, statistical, machine learning, and hybrid. Today, thanks to the powerful advancement of intelligent systems, advanced deep learning models have become an indispensable tool in the realization of accurate forecasting models.

Organizers: Prof. Peter Glösekötter—Peter Glösekötter is Professor at the Department of Electrical Engineering and Computer Science at FH Münster. Dr. Joseph Moerschell—Joseph Moerschell is Professor of Electronics and Mechatronics in HES-SO, the University of Applied Sciences of Western Switzerland, in Sion. Prof. Ignacio Rojas—Ignacio Rojas is Professor at the Department of Computer Engineering, Automation and Robotics at the University of Granada.  Prof. Tilman Sanders —Prof. Tilman Sanders is Professor at the Department of Electrical Engineering and Computer Science at FH Münster. Prof. Markus Gregor —Markus Gregor is Professor at the Department of Engineering Physics at the FH Münster University of Applied Sciences. Prof. Sarah Kirschke —Sarah Kirschke is Professor at the Münster University of Applied Sciences. Prof. Ruxandra Stoean —Ruxandra Stoean is Associate Professor at the Department of Computer Science, Faculty of Sciences, University of Craiova, Romania.

SS8. Innovations in Time Series Forecasting: Leveraging Machine Learning and Big Data in Econometrics.

Recent advancements in time series forecasting have profoundly transformed the landscape of econometric analysis. As the volume, variety, and velocity of data continue to expand, traditional econometric models face increasing challenges in capturing the underlying complexities of economic phenomena. The integration of machine learning techniques with time series forecasting represents a paradigm shift in addressing these challenges, offering substantial improvements in prediction accuracy, model flexibility, and the ability to extract insights from high-dimensional datasets.

This special session will focus on cutting-edge innovations in time series forecasting, with a particular emphasis on the utilization of machine learning algorithms and the handling of large-scale datasets. Contributions will explore the application of deep learning, reinforcement learning, and ensemble methods in the context of economic and financial forecasting. Furthermore, we will delve into the challenges posed by big data, such as high-frequency data analysis, data quality issues, and computational efficiency.

Key topics will include, but are not limited to, high-dimensional time series forecasting, nonlinear and regime-switching models, Bayesian econometrics, machine learning integration in econometric frameworks, and robust estimation techniques for handling structural breaks and volatility clustering. Particular emphasis will be placed on the challenges posed by economic uncertainty, financial market instability, and the increasing availability of high-frequency and big data sources.

Organizer: Prof. L. Whan, Professor of Economics, University of York.

SS9. Real macroeconomic monitoring and forecasting.

Topics: decisions, financial stability, and strategic economic planning. In an increasingly interconnected and volatile global economy, traditional econometric models must evolve to incorporate new data sources, advanced estimation techniques, and real-time analytical frameworks. This special session aims to explore recent innovations in macroeconomic forecasting, with a particular emphasis on real-time data analysis, nowcasting techniques, and the integration of high-frequency economic indicators.

Organizer: Prof. S. Chin, Professor of Economics, United International University, Bangladesh.

SS10. Environmental and Ground Deformation Monitoring via Remote Sensing Time Series Data.

Monitoring land cover/use and ground deformation is a crucial task in environmental hazards and management to ensure a sustainable environment, safety of lives and infrastructure. Many factors can cause the land surface or ground to change or deform, such as earthquakes, slow-moving landslides, subsidence due to groundwater exploitation or underground mining, volcanic unrest, wildfires, and others. Recent advances in remote sensing techniques have created a great opportunity to effectively and continuously monitor the land surface. These techniques include Optical Satellite Remote Sensing Data, Interferometric Synthetic Aperture Radar (InSAR), Persistent Scatterer InSAR (PS-InSAR), Light Detection And Ranging (LiDAR), Global Navigation Satellite Systems (GNSSs), Close-Range Photogrammetry (CRP), and Robotic Total Station (RTS). Spatio-temporal land surface monitoring can be rigorously performed by analysing the time series acquired from these techniques. Processing such time series can also be very challenging for several reasons, such as non-uniform sampling, biases as a result of preprocessing, and atmospheric/environmental noise.

The aim of this Special Issue is to collect papers (original research articles and review papers) that offer insights into effectively monitoring land cover/use change and land deformation using remotely sensed time series data.

This Special Issue will welcome manuscripts that link the following themes:

• New time series analysis methods for land cover and ground deformation monitoring;
• Applications of existing time series or data processing methods in Earth’s surface monitoring;
• A combination of different techniques, such as InSAR, LiDAR, GNSS, and CRP, for ground deformation monitoring and change detection using advanced artificial intelligence models.

Authors may choose one of our special issues:

Ground Deformation Monitoring via Remote Sensing Time Series Data in Land (MDPI) https://www.mdpi.com/journal/land/special_issues/2A61OI7856, accessed on 14 December 2025.

OR:

Earth and Environmental Sciences: Earth Surface Monitoring Using Remote Sensing Data and Artificial Intelligence in Discover Applied Sciences (Springer) https://link.springer.com/collections/hejdcjdahe, accessed on 14 December 2025.

Organizer: Prof. Ebrahim Ghaderpour and Prof. Dr. Paolo Mazzanti, Department of Earth Sciences, Sapienza University of Rome, Italy.

4. Plenary Talk in ITISE-2025

In this edition of ITISE, we are honoured to have the following invited speaker:

• Prof. Vilem Novak, University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, Czech Republic.
  Prof. Vilem Novak, Ph.D., DSc., is the founder and former director of the Institute for Research and Applications of Fuzzy Modeling of the University of Ostrava, Czech Republic. The institute (established in 1996) is one of the world-renowned scientific workplaces that significantly contributed to the theory and applications of fuzzy modelling. Prof. V. Novak obtained a PhD in mathematical logic at Charles University, Prague in 1988; DSc. (Doctor of Sciences) in computer science in the Polish Academy of Sciences, Warsaw in 1995; and Full Professor status at Masaryk University, Brno in 2001. His research activities include mathematical fuzzy logic, approximate reasoning, mathematical modelling of linguistic semantics, fuzzy control, analysis and forecasting of time series, and various kinds of fuzzy modelling applications. He belongs among the pioneers of the fuzzy set theory. He was the general chair of the VIIth IFSA’97 World Congress, Prague, and of the international conferences EUSFLAT 2007, Ostrava, and EUSFLAT 2019, Prague. He is a member of the editorial boards of several scientific journals. He is often invited to give plenary talks at international conferences and give lectures in universities worldwide.
  He is the author or co-author of six scientific monographs, two edited monographs, and over 310 scientific papers with almost 9000 citations. He was awarded in the International Conference FLINS 2010 in China and obtained the title “IFSA fellow” in 2017 for his scientific achievements. He is currently the vice-president of IFSA.
  Title of the presentation: Non-statistical methods for processing of time series and mining information from them.
  We will present special techniques of fuzzy modelling suitable for applications in time series processing, namely the Fuzzy Transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). The F-transform is applied to estimation of the trend or trend-cycle of time series, and to estimation of the slope of time series over an imprecisely specified area. Our methods are based on the decomposition of the time series into four components: trend, cycle, seasonal component, and random disturbances. The fuzzy transform makes it possible to find arbitrary shape of the trend or trend-cycle. It has been proved that using the F-transform, we can eliminate seasonal component and significantly reduce noise. Moreover, the computational complexity is low.
  Our methods also have applications in mining information from time series. Among them, let us mention reduction in dimensionality, finding intervals of monotonous behaviour and their characterization using expressions of natural language, measure of similarity between time series, or automatic summarization of knowledge about time series. We also suggest a powerful method for detection of structural breaks. The found structural breaks are also statistically tested.
  We will compare our methods with traditional methods and demonstrate that they can on one hand successfully compete with them and on the other hand, both kinds of methods can be combined to increase the effectivity of the processing of time series.
• Prof. K. Nikolopoulos, Deng (NTUA), ITP (Northwestern), Durham University, DUBS and IHRR.
  Dr. Konstantinos (Kostas) Nikolopoulos is Professor in Business Information Systems and Analytics at Durham University Business School.
  Dr. Nikolopoulos studied Electrical and Computer Engineering at the National Technical University of Athens (EM$\mathsf{\Pi }$) in his native Greece (D.Eng. 2002, Dipl. Eng. 1997). He further completed the International Teachers Programme (ITP) at Kellogg School of Management at Northwestern University (2011). His research interests are Forecasting, Analytics, Information Systems, and Operations.
  Dr. Nikolopoulos was Professor of Business Analytics/Decision Sciences at Bangor University for a full decade, and completed three tenures as the College Director of Research (Associate Dean for Research & Impact) for the College of Business, Law, Education, and Social Sciences (2011–2018) in charge of the REF2014 submission for the Business and the Law school. Before that, he was Lecturer and Senior Lecturer in Decision Sciences at the University of Manchester, a Senior Research Associate at Lancaster University, and the CTO of the Forecasting and Strategy Unit (www.fsu.gr) in the Electrical and Computer Engineering Department of the National Technical University of Athens (1996–2004). He has also held fixed-term teaching and academic appointments in the Indian School of Business, Korea University, Univerity of the Peloponnese, Hellenic International University, RWTH Aachen, Lille 2, and more recently in Kedge Business School.
  Professor Nikolopoulos is an Associate Editor of Oxford IMA “Journal of Management Mathematics” and the “Supply Chain Forum, an International Journal” (Taylor & Francis); he is also the Section Editor-In-Chief for the “Forecasting in Economics and Management” section in the MDPI open access journal Forecasting.
  Professor Nikolopoulos is currently Co-Investigator in two major research grants for (a) the GCRF; South Asia Self Harm research capability building initiative (SASHI) project funded by the Medical Research Council in UK (2017–2021), http://sashi.bangor.ac.uk/, accessed on 14 December 2025, and (b) the H2020-FETPROACT; Radioactivity Monitoring in Ocean Ecosystems (RAMONES) funded by the EU (2021–2025). In the past, he has succesfully bid as PI for more than £0.5 M of research grants through the forecasting laboratory (forLAB) he founded and directed in Prifysgol Bangor University in Wales, UK.
  Professor Nikolopoulos’ work has been consistently appearing in the International Journal of Forecasting (29 outputs) but also in journals for broader audiences including the Journal of Operations Management, the European Journal of Operational Research, and the Journal of Computer Information Systems.
  Title of the presentation: The Computer and the Brain.
  In light of the frantic welcome of AI, and all the changes it will bring, we are revisiting the long-standing battle of, on the one hand, the Computer, the machine, the artificial brain, AI…, and on the other hand, the real Brain, natural human intelligence, HI. We do so in the context of forecasting in business, finance, and economics and we highlight the last few areas where the human brain can bring true excellence, and we also translate this into lessons for implementation and (information) systems designers and developers. We do also discuss issues of accountability of forecasting errors.
• Prof. Martin Wagner, Professor of Economics at the University of Klagenfurt, Vice-Dean of the Faculty of Management, Economics and Law. Chief Economic Advisor, Bank of Slovenia Senior Fellow, Macroeconomics and Business Cycles, Institute for Advanced Studies Managing Editor, German Economic Review.
  Martin Wagner currently is Professor of Economics at the University of Klagenfurt, Chief Economic Advisor at the Bank of Slovenia, and Fellow of the Macroeconomics and Economic Policy group at the Institute for Advanced Studies, Vienna. From October 2017 until the end of 2018, he was Chief Economist of the Bank of Slovenia, being on leave from Technical University Dortmund, where we was Professor of Econometrics and Statistics in the Faculty of Statistics of the Technical University Dortmund from 2012 until 2019. He was educated in Vienna, at the Technical University and the Institute for Advanced Studies, obtaining Diplomas in Mathematics (1995) and Economics (1998), as well as his Doctorate (2000). He obtained his Habilitation in Economics in 2007 at the University of Bern. Martin Wagner has worked at the Technical University of Vienna, the Institute for Advanced Studies in Vienna, and the University of Bern, and was Professor of Econometrics and Empirical Economics at the University of Graz before his arrival in Dortmund. Visiting positions have taken him to Princeton University and the European University Institute in Florence.
  His work has been published, amongst other outlets, in Journal of Econometrics, Econometric Theory, Journal of Applied Econometrics, Econometric Reviews, Econometrics, Oxford Bulletin of Economics and Statistics, Journal of Empirical Finance, Economics of Transition, and Ecological Economics.
  Title of the presentation: Integrated Modified Least Squares Estimation and (Fixed-b) Inference for (Systems of) Cointegrating Multivariate Polynomial Regressions.

These plenary lectures strengthened the aim of this conference for the diffusion and the discussion of high-quality researches from some of the most recognized scientists in these fields.

5. ITISE-2025 and MDPI Engineering Proceedings and MDPI Computer Sciences & Mathematics Forum

The prediction and analysis of time series is known to be an extremely multidisciplinary field of research that encompasses a broad spectrum of methods, theoretical foundations, and areas of application. This diversity reflects the complexity of the phenomena under investigation and the need to integrate knowledge from fields such as statistics, computer science, engineering, mathematics and econometrics, to name but a few.

Given this multidisciplinary nature, the ITISE-2025 conference has adopted a strategic approach to the dissemination of scientific contributions. In particular, a careful selection of papers to be included in two different conference proceedings has been assembled to target each paper to the most appropriate academic and professional audience according to its thematic focus.

For contributions focusing primarily on engineering topics—such as system modelling, signal processing, control theory and applied computing techniques—the MDPI journal Engineering Proceedings has been selected as the ideal platform for presenting research results at the interface between theoretical innovation and practical implementation in engineering contexts.

In contrast, the MDPI journal Computer Sciences & Mathematics Forum was chosen for papers focusing on mathematics and econometrics, covering areas such as statistical inference, stochastic processes, time series modelling, forecasting techniques, and their applications in economic and financial systems. This event provides a rigorous academic setting that is well suited for the dissemination of research results based on formal mathematical analysis and quantitative methods.

As in previous editions, ITISE-2025 features a selection of papers published in Engineering Proceedings and Computer Sciences & Mathematics Forum. The first volume of Engineering Proceedings to feature contributions from the congress was published for ITISE-2021 [1], and then again for ITISE-2022 [2], ITISE-2023 [3], and ITISE-2024 [4].

In submitting conference proceedings to Engineering Proceedings and Computer Sciences & Mathematics Forum, the Volume Editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review performed by the Volume Editors. Reviews are conducted by expert referees adhering to the professional and scientific standards expected of a proceedings journal. The type of peer review was single-blind, and the conference submission management system used was Easychair (194 contributions was submitted), presenting in these proceedings a selection of such contributions, which have on average been reviewed by at least two expert reviewers. A total of 19 contributions were selected for the MDPI journal Engineering Proceedings and 38 contributions for the MDPI journal Computer Sciences & Mathematics Forum at ITISE-2025.