New Developments in Time Series and Forecasting, ITISE-2023 †
2. Main Topics of ITISE
- Time series analysis and forecasting
- Nonparametric and functional methods;
- Vector processes;
- Probabilistic approaches to modeling macroeconomic uncertainties;
- Uncertainties in forecasting processes;
- 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.
- Econometrics and forecasting
- Econometric models;
- Economic and econometric forecasting;
- Real macroeconomic monitoring and forecasting;
- Advanced econometric methods.
- 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.
- High-dimension and complex/big data
- Local vs. global forecasts;
- Dimension reduction techniques;
- Forecasting complex/big data.
- 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-2023
- SS1. Advances in time series analysis and forecasts in Engineering Sciences.
- Parametrical versus non-parametric approaches for data series modeling in engineering sciences;
- Critical evaluation and comparisons of alternative approaches for experimental time series modeling;
- New techniques for spatial data analysis;
- New software for data analysis—development and applications for solving engineering problems;
- Soft computing and fuzzy techniques for engineering time series modeling;
- Environmental time series modeling (precipitation, temperature, pollution).
- Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration
- Hydrology (MDPI)—indexed within Scopus, ESCI (Web of Science)—tracked for Impact Factor—CiteScore 3.6—https://www.mdpi.com/journal/hydrology (12 July 2023)
- SS2. Advanced econometric methods for Economic analysis and Finance
- SS3. Cryptocurrency time series modelling and forecasting
- SS4. Artificial Intelligence and Sustainability
4. Plenary Talk in ITISE-2023
- Prof. Eamonn Keogh, Distinguished Professor, Department of Computer Science and Engineering University of California Riverside. Title of the presentation: Irrational Exuberance: Has Deep Learning Contributed Anything to Time Series problems?
- Prof. Martin Wagner, 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. Title of the presentation: Sources and Channels of Nonlinearities and Instabilities of the Phillips Curve: Results for the Euro Area and Its Member States
- Prof. Daniel Peña Sanchez De Rivera, Professor at Universidad Carlos III de Madrid. Department of Statistics. Madrid (Spain). Title of the presentation: Finding the Number of Clusters in Time Series
5. Peer-Review Statement and MDPI Engineering Proceedings
Conflicts of Interest
- Rojas, I.; Rojas, F.; Herrera, L.; Pomares, H. The 7th International Conference on Time Series and Forecasting; MDPI Proceedings; MDPI: Basel, Switzerlands, 2022; ISBN 978-3-0365-1732-2. ISSN 2504-3900. [Google Scholar]
- Rojas, I.; Pomares, H.; Valenzuela, O.; Rojas, F.; Herrera, L.J. The 8th International Conference on Time Series and Forecasting; Engineering Proceedings; MDPI: Basel, Switzerlands, 2022; ISBN 978-3-0365-5452-5. ISSN 2673-4591. [Google Scholar]
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Valenzuela, O.; Rojas, F.; Herrera, L.J.; Pomares, H.; Rojas, I. New Developments in Time Series and Forecasting, ITISE-2023. Eng. Proc. 2023, 39, 101. https://doi.org/10.3390/engproc2023039101
Valenzuela O, Rojas F, Herrera LJ, Pomares H, Rojas I. New Developments in Time Series and Forecasting, ITISE-2023. Engineering Proceedings. 2023; 39(1):101. https://doi.org/10.3390/engproc2023039101Chicago/Turabian Style
Valenzuela, Olga, Fernando Rojas, Luis Javier Herrera, Hector Pomares, and Ignacio Rojas. 2023. "New Developments in Time Series and Forecasting, ITISE-2023" Engineering Proceedings 39, no. 1: 101. https://doi.org/10.3390/engproc2023039101