The use of dynamic stochastic models in different areas has been increasing in recent years. In particular, Continuous Time models arise naturally for describing physical/economics/social phenomena that are observed on irregularly spaced time grids. Theoretical advantages have not been yet fully exploited in practice. Indeed, in recent literature we still find applications mainly based on discrete-time models partially due to the well- established estimation procedures. This special session is devoted to the collection of the latest developments related to the class of Continuous-Time models. In particular, papers focused on but not limited to the following topics of interest are welcome:

• Time-series forecasting model for Coronavirus outbreak and other epidemics
• Estimation and Simulation Algorithms for Continuous Time Models.
• New Continuous Time Models and related statistical properties of estimators.

Organizers:
Lorenzo Mercuri is an Associate Professor at the University of Milano. He holds a PhD in Mathematical Finance from the University of Milano-Bicocca. He is currently involved in an international research project financed by CREST Japan. His research interests include Stochastic processes, Option Pricing and Numerical Methods applied to Finance. Department of Economics, Management and Quantitative Methods, University of Milan, Via del Conservatorio 7 Milan, Italy.
Prof. Edit Rroji is an Assistant Professor at the University of Milano-Bicocca. She holds a PhD in Mathematical Finance from the University of Milano-Bicocca. Previously, she has worked at the University of Trieste and at Politecnico di Milano. Her research interests include the modeling of financial time series and actuarial mathematics. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1 Milan, Italy.

Recently, we are dealing with a large increase of interest in the time series analysis, i.e. data evolving in time. Such data come from various sources, such as e.g., automated systems, small scale computing devices, sensor networks, and are marked by different types and degrees of uncertainty: they might be imprecise, ambiguous, may include missing values, their quality may depend on the source or conditions, etc. Soft modeling and soft computing methods enable to address uncertainty because they are less rigid than traditional approaches. The desired methodology should combine different types and aspects of uncertainty, including randomness, imprecision, ambiguity, etc. By integrating fuzzy logic, probability theory and other approaches, more robust and interpretable models and tools can be developed that better reflect the uncertainty related to the observed data. The aim of this Special Session is to bring together theorists and practitioners who apply soft methods in time series analysis and forecasting to exchange ideas and discuss new trends that enrich traditional approaches and tools. Topics of interest include but are not limited to:

Soft methods for time series; fuzzy statistics for time series; recursive processing of large data; uncertainty modeling; evolving neural and neuro-fuzzy networks; analysis of censored or missing data; nalysis of fuzzy data; zzy random variables; fuzzy regression methods; granular computing; imprecise probabilities; interval data; machine learning; possibility theory; random sets; rough sets; fuzzy-rough sets; soft computing; statistical software for imprecise data

Organizers:
Prof. Katarzyna Kaczmarek-Majer Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland. Katarzyna Kaczmarek-Majer is an Assistant Professor at the Systems Research Institute of the Polish Academy of Sciences. Her research interests cover time series analysis, soft computing, computational statistics, sensor data analysis and medical applications. Some of her works have been awarded at scientific conferences. She combines effectively her theoretical research with involvement in scientific projects for medicine, environmental protection, etc. She is an Associate Editor of Journal of Intelligent and Fuzzy Systems, and a vice-coordinator of the eHealth section of Polish Information Processing Society.
Prof. Przemyslaw Grzegorzewski Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland and Systems Research Institute, Polish Academy of Sciences. Przemyslaw Grzegorzewski is a Full Professor at the Faculty of Mathematics and Information Science of Warsaw University of Technology and Systems Research Institute of the Polish Academy of Sciences. His areas of expertise include mathematical statistics, statistical decisions with imprecise data, fuzzy sets, data mining, soft computing, etc. Having authored several books, he edited several volumes and special issues of scientific journals. Overall, he published more than 170 papers in scientific journals, edited volumes and conference proceedings. He is a co-founder and a member of the Executive Board of the International Conference on Soft Methods in Probability and Statistics (SMPS).
Prof. Daniel Peralta IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium. Daniel Peralta is a Postdoctoral Researcher at the Department of Information Technology of Ghent University. His research interests involve machine learning, time series analysis, biological imaging data analysis, biometrics, large-scale datasets and parallel and distributed computing. He has published more than 20 articles in international journals.

Motivation
Time series forecasting is an important problem in many domains such as business, industry, economics, engineering, and science. It is relevant and challenging as the time series expressing real-world phenomena are in many cases very complex and stochastic, including trends, multiple seasonality, and significant random fluctuations. Therefore, time series forecasting is an active research area that has received a considerable amount of attention from researchers and practitioners for many years. Over the past few decades, neural networks have been successfully used in this field due to their ability to capture different patterns and high expressive power to solve non-linear stochastic forecasting problems. However, in practice, it is quite challenging to properly determine an appropriate architecture and parameters of neural networks as well as the training process and time series representation so that the resulting forecasting model can achieve sound performance for both learning and generalization. Practical applications of neural forecasting models bring additional challenges, such as dealing with big, missing, distorted, and uncertain data. In addition, interpretability, explainability, and causality are paramount qualities that neural methods should aim to achieve if they are to be applied in practice.
Scope
This Special Session focuses on neural models and their application in a diverse range of forecasting areas and problems including probabilistic and multi-step forecasting. The papers are expected to report substantive results on a wide range of neural forecasting models (variants of MLP, CNN, RNN and others), discussing their architectures and training procedures, the conceptualization of a problem, time series representation, feature engineering, critical comparisons with existing techniques, and interpretation of results. Specific attention will be given to recently developed neural forecasting models including deep and hybrid solutions.

Organizers:
Prof. Grzegorz Dudek Department of Electrical Engineering, Czestochowa University of Technology, 42-200 Czestochowa, Al. Armii Krajowej 17, Poland,
Grzegorz Dudek received his PhD in electrical engineering from Czestochowa University of Technology, Poland, in 2003 and habilitation in computer science from Lodz University of Technology, Poland, in 2013. Currently, he is an associate professor at the Department of Electrical Engineering, Czestochowa University of Technology. He is the author of two books concerning machine learning methods for load forecasting and evolutionary algorithms for unit commitment and over 100 scientific papers. He came third in the Global Energy Forecasting Competition 2014 (price forecasting track). His research interests include pattern recognition, machine learning, artificial intelligence, and their application to practical classification, regression, forecasting and optimization problems.
Mr. Slawek Smyl Meta 1 Hacker Way, Menlo Park, CA 94025, USA
Slawek Smyl received the M.Sc. degree in Physics from Jagiellonian University, Kraków Poland in 1988, the M.Eng. degree in Information Technology from RMIT University, Melbourne VIC Australia in 1997. He is currently a Quantitative Engineer with Meta Technologies working in the area of time series forecasting. Mr. Smyl has ranked highly in forecasting competition: he won the Computational Intelligence in Forecasting International Time Series Competition 2016, got a third place in the Global Energy Forecasting Competition in 2017, and won the M4 Forecasting Competition in 2018.

Summary
One of the crucial points in the transition to a sustainable society is the ability to manage the risks of extreme events and disasters, in particular for the advancement of adaptation to climate change. The scientific literature studies in deep the relationship between climate change and extreme weather events and the implications of these events for society and sustainable development. Actually, the nature and severity of the impacts of climatic extremes depend not only on the extremes themselves, but also on exposure and vulnerability. The interaction of climatic, environmental and human factors can lead to impacts and disasters, so it is primary to assess the important role that non-climatic factors play in determining the impacts. Suggested topics of this special session include but are not limited to: extreme value theory, copula functions, parametric versus nonparametric approach in extreme events analysis, deep-learning methods, time-series modelling based on LSTM.

Organizer:
Prof. Giovanni De Luca Professor of Economic Statistics, University of Naples Parthenope, www.researchgate.net/profile/Giovanni_De_Luca4

Summary
The recent circumstances, oil price fluctuations, Covid-19, etc., have caused volatile behavior in demand for energy. Therefore, the need to model and forecast energy demand and equip policymakers with information to ground their decisions on necessitates the availability and information on comparative applicability of relevant techniques. Hence, research focused on the new modeling and forecasting techniques or comparing the features of the existing ones is worth to elaborate.

Organizer:
Prof. Jeyhun I. Mikayilov Energy and Macroeconomics Department, King Abdullah Petroleum Studies & Research Center, Office#: RC 1044, http://www.kapsarc.org .

Climate:
In climate studies causality is often invoked by claiming that a phenomenon that generate movements in one time series will later generate movements in another time series, and this latter series causes weather or climate changes. (Example: the pair, the Pacific decadal variability and El Niño).

Economics:
In economics a leading relation in one series is often used to predict changes in another (but not by causation). (Example: a decline in working hours in the industry is used to predict a coming recession in the economy.)

Tools -of- the trade:
Cross correlation analysis and all the problems associated with it. Granger causality. Sugihara causality. And my own “high- resolution lead-lag method. A comparison among the methods on a common example would be nice.

Organizer:
Prof. Knut Lehre Seip Department of Technology, Arts and Design, Oslo Metropolitan University, Oslo , Norway

Motivation and objectives for the session.
The term hydrological risk may comprise different understandings depending on the type of project/research and the risk assessment goals. Extreme high-and low-flow events, also referred to as floods and droughts, respectively, have large natural, societal, and economic impacts. On the global scale, disastrous and economic losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements. The impacts of low-flow events can be recognised in among others the sanitary sewer system, crop production and the hydropower sectors. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological and hydraulic models’ structure on the magnitude and timing of simulated extreme rainfall-runoff events. This Special Issue is mainly aimed to welcome contributions to enhance the characterization and predictive capacity of extreme rainfall-runoff events. This is also aimed to achieve a sustainable maintenance and operation of urban rivers and their associated sanitary-rainwater sewer system, under high hydrological risk. For that, innovative methodologies and tools that can synchronize Historical with Real Time data is designed are welcomed.

Organizer:
Prof. José-Luis Molina IGA Research Group. Salamanca Univ, High Polytech Sch Engn, Avila Area Hydraulic Engn, Av Hornos Caleros 50, Avila 05003, Spain

Regarding academic background, Dr. José-Luis Molina has a degree in Civil Engineering, obtained in 2015, a degree in Environmental Sciences obtained in 2002 from the University of Granada (Spain) and three Masters related to the Environment and Water Management and Hydraulics. In addition, Dr. Molina obtained a PhD in Water Management in 2009 at the Geological and Mining Institute of Spain (IGME) and the University of Granada with the title "Integrated Analysis and Management Strategies of Aquifers in Semi-arid Areas. Application to the case study of the Altiplano (Murcia, SE Spain)". In the research dimension, he has worked internationally with many research groups, such as the Oxford University Centre for the Environment (OUCE), the Department of Land Water and Air at the University of California-Davis, etc. In 2010 he obtained a postdoctoral research position at the Centre for Integrated Assessment and Management of Watersheds (ICAM) at the Australian National University (ANU), and after that, another postdoctoral position at the Research Institute of Water and Environmental Engineering (IIAMA) at the Polytechnic University of Valencia (UPV). In addition, Professor Molina has about 45 publications in high impact SCI research journals such as "Journal of Hydrology", "Environmental Modelling and Software", "Water Resources Management". He holds an Associate Editor position of Journal of Hydrology (ELSEVIER Q1) as well as Editor in Chief of 2 Special Issues for the Journal Sustainability (MDPI Q2). He is also a reviewer of numerous research journals in the first quartile in the areas of Civil Engineering, Environment and Water Resources. He is also an evaluator of ANEP projects. As for teaching, he currently holds a position as Associate Professor in the Area of Hydraulic Engineering of the Department of Cartographic and Land Engineering of the University of Salamanca. This teaching has been developed mainly in the areas of Hydraulic Engineering and External Geodynamics, having at present a full-time teaching experience of about 12 years. He is currently the Director of the Research Group in Engineering and Water Management (IGA) by the University of Salamanca. He is currently the Director of the MEng “Modelización de Sistemas Hídricos” at University of Salamanca. Regarding transferring of knowledge he has been the IP of about 10 contracts since 2016 with a total amount of about 300.000 €.

Considering functional data anasysis (FDA) being one of the important research fields in Statistics, whether there is a spcial session with tile " Functional time series analysis and application"

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

He is the Guest Editor of following two Special Issues: 1. Advances in Time Series Analysis (Sensors, MDPI, Impact Factor: 3.58, Q1): https://www.mdpi.com/journal/sensors/special_issues/A_TSA 2. Artificial Intelligence and Sustainability (Sustainability, Impact Factor: 3.25, Q1): https://www.mdpi.com/journal/sustainability/special_issues/sustai_artificalintelligence

For the Special Issue in Advances in Time Series Analysis (Sensors, MDPI), the message from the Guest Editors is: "Time series analysis has recently attracted wide attention in many fields of science, such as remote sensing, hydrology, geodesy, geophysics, astronomy, finance, and medicine. Time series analysis is a very challenging task and often requires pre-knowledge of the data. For example, time series obtained from Earth observation data are o􀀷en unevenly sampled (equally spaced) and have uncertainties due to various reasons, such as sensor defects and atmospheric effects. Therefore, new techniques that can consider such uncertainties, as well as irregularities in sampling, are highly demanded. In this Special Issue, we welcome:
1) Manuscripts describing applications of the methods mentioned above for analyzing time series obtained from various sensors;
2) Manuscripts demonstrating new time series analysis techniques and/or applications of existing methods."
More information in this flyer

Organizer:
Prof. Ebrahim Ghaderpour University of Calgary, 2500 University Dr. NW Calgary, Alberta, Canada T2N 1N4

He is the Guest Editor of following Special Issue: 1. Call for Special Issue "Inclusive Science of the Total Environment" (Journal: Sustainability) https://www.mdpi.com/journal/sustainability/special_issues/Total_Environment

For the Special Issue in Inclusive Science of the Total Environment (Sustainability MDPI), the message from the Guest Editors is: "In the wake of persistent global push against climate change and other environmental related challenges confronting human existence in the 21st century, diverse policy drive are being recommended toward attaining a sutainable environment. Considering the complexity of the subject of environmental sustainability, this discourse remains an evolving subject because of the inter-connectednes of the environmental aspects (including ecological systems) with all human aspects (including science and socioeconomic activities). Thus, it suffice to suggest that ‘Science of the Total Environment’ is a necessary pathway to understanding environmental sustainability. More importantly, the ‘inclusivity of Science of the Total Environment’ becomes a necessary and sufficient pathway to unearth age-long environmental challenges. Indicatively, the attainability of a healthy state of the environment is contingent on not only natural activites, but extensively on humans’ environmental responsibility, scientific and technological advancement, and socioecomically just factors (Intergovernmental Panel on Climate Change (IPCC, 2020); United Nations Environmental Programme (UNEP)). In specific, the entire state of the environment has been largely associated with the inclusivity of the 17 Sustainable Development Goals (SDGs) of the United Nations Development Programme (UNDP) (UNDP, 2020). This account for the reason the aspects of the state of the environment has consistently been associated with health, waste management and pollutant emission across the sectors (Bekun, Alola & Sarkodie, 2019), human behaviour and organizational practice (Cop, Alola & Alola, 2020), societal and cultural norms (Aldieri et al., 2019), agricultural practices (Alola & Alola, 2018), and many more. In essence, this special issue is geared toward inviting scientific contributions from authors on the subject that addresses the importance of inclusivity in attaining a desirable state of the total environment. An expected manuscriopt should address but tot limited to the sub-areas of health, waste management, land, pollutant emission, carbon capture and sequensation, energy, human behaviour, and innovation.."

Organizer:
Dr. Andrew Adewale Alola Department of Economics and Finance, Istanbul Gelisim University, Istanbul 34310, Turkey
Interests: economics; energy, environmental economics; fuzzy set and logic

The cryptocurrency market, or rather more generally the cryptoasset market, is a field that is rapidly and steadily growing. Similarly, the literature regarding this topic has also enormously grown over the last few years. What distinguishes this market from the tradidional financial market is that it operates 24/7 and every day, contrarily to the majority of traditional financial markets that operate within particular hours and only on business days. Moreover, what we already know about cryptoasset market is that it is characterized by wide heterogeneity, in terms of different aspects - both from the perspective of the supply and the demand.

Most of the studies and most of the available methodologies for the analysis of the behavior of cryptoasset prices is dedicated mainly for the data of daily frequency. Therefore, the literature using the daily cryptoasset data is already quite exhausted. On the other hand, there are not many studies that make use of high-frequency data. It is important to distinguish the aggregated high-frequency data (e.g. the data that contain weighted-average of prices/volumes from multiple major cryptocurrency exchanges, available for instance on coinpaprika.com up to 5-min frequency) and the tick-to-tick on-exchange data that are also openly available. Therefore, such availability of high-frequency tick-to-tick data gives a great opportunity to not only develop the cryptoasset research field but also the field of high-frequency financial data in general, which is constantly growing over the last decade or two.

Therefore, the aim of this session is to discuss the studies that focus on the usage of high-frequency cryptoasset data, since this field requires more attention in the literature because of the large gaps up to this point.

Organizer:
Mr. Damian Zięba University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance, Warsaw, Poland.

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. This section invites works and works-in-progress from both academia and industrial partners to share and present ideas which could contribute to understanding and hopefully subsiding this outbreak which has evolved to global pandemic.

Papers related but not limited to the following topics of interest are solicited:

• Time-series forecasting model for Coronavirus outbreak and other epidemics
• Machine learning, AI and Big Data for modeling epidemic outbreaks
• Decision support tools and optimization for modelling and controlling epidemics
• Hybrid models of forecasting and other states-of-arts for predicting epidemics
• Social media and text mining for predicting lifetimes and trends of epidemics
• Data analytics and correlations of past epidemics and Coronavirus outbreak
• Forecasting of post-epidemic stock markets and worldwide economy impact
• Other issues related to the epidemics: Government policy, medical resources, etc.

Organizers:
Prof. Simon James Fong, University of Macau, Macau SAR.
Prof. Nilanjan Dey, Techno India College of Technology, India
Prof. Rubén González Crespo, Universidad Internacional de La Rioja, Logroño, Spain
Prof. Enrique Herrera-Viedma, University of Granada, Spain
Prof. Antonio J. Tallón-Ballesteros, University of Huelva, Huelva, Spain

MOTIVATION

Over the past few decades, application of simple statistical procedures with considerable heuristic or judgmental input was the beginning of forecasting, then in the 80’s, sophisticated time series models started to be used by some of the dynamic system operators, and these approaches, were to become pioneering works in this field. Soft computing methods including support vectors regression (SVR), fuzzy inference system (FIS) and artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly in order to unify the field of forecasting and to bridge the gap between theory and practice, making forecasting useful and relevant for decision-making in many fields of the sciences. The purpose of this session is to hold smaller, informal meetings where experts in a particular field of forecasting can discuss forecasting problems, research, and solutions in the field of automatic control. There is generally a nominal registration fee associated with attendance. This session aims to debate in finding solutions for problems facing the field of forecasting. We wish to hear from people working in different research areas, practitioners, professionals and academicians involved in this problematic.

SCOPE

The session seeks to foster the presentation and discussion of innovative techniques, implementations and applications of different problems that are Forecasting involved, specially in real-world problems applied to control and automation.
• Time Series Analysis
• Time Series Forecasting
• Evaluation of Forecasting Methods and Approaches
• Forecasting Applications in Business, Energy and Price Demand, Hydrology, etc.
• Impact of Uncertainty on Decision Making
• Seasonal Adjustment
• Multivariate Time Series Modelling and Forecasting
• Marketing Forecasting
• Economic and Econometric Forecasting

Journal of Forecasting .

Dr.Cristian Rodriguez, the Guest Editor of the Special Issue on "Bayesian Time Series Forecasting" at the Journal of Forecasting and the organizer of the Special Session in ITISE-2022: "SS2. Computational Intelligence for Applied Time Series Forecasting in Complex Systems (CIATSFCS)".

Organizers:
Prof. Cristian Rodriguez Rivero,University of Amsterdam, c.m.rodriguezrivero@uva.nl, IEEE CIS, Co-Founder LA-CIS.
Prof. Alvaro Orjuela Cañón,Universidad del Rods, dorjuelaco@ieee.org, IEEE CIS, Co-Founder Board of Directors of LA-CIS.
Prof. Héctor Daniel Patiño,Universidad Nacional de San Juan, Argentine,dpatino@inaut.unsj.edu.ar. IEEE CIS.
Prof. Julián Antonio Pucheta,Universidad Nacional de Córdoba, Argentine,jpucheta@efn.uncor.edu.
Prof. Gustavo Juarez,Universidad Nacional de Tucumán, Argentine, juarez.gustavo@ieee.org, IEEE CIS.
Prof. Leonardo Franco,School of Engineering in Informatics, University of Malaga, Spain. lfranco@lcc.uma.es. IEEE CIS.