Advances in time series forecasting: innovative methods and applications

AIMS Mathematics, 9(9): 24163–24165. DOI: 10.3934/math.20241174 Received: 07 August 2024 Revised: 07 August 2024 Accepted: 14 August 2024 Published: 14 August 2024 https://www.aimspress.com/journal/Math Editorial Advances in time series forecasting: innovative methods and applications J. F. Torres1,*, M. Martinez-Ballesteros2, A. Troncoso1 and F. Martinez-Alvarez1 1 Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain 2 Department of Computer Science, University of Seville, ES-41012 Seville, Spain * Correspondence: Email: [email protected]. Time series forecasting plays a critical role in various domains, including finance, economics, environmental science, and healthcare. Time series forecasting has undergone significant evolution with the increasing availability of data and advancements in machine learning and statistical methods. This special issue aimed to bring together the latest advances, innovations, and research in the field of time series forecasting. Thus, a significant theme in this collection is the application of advanced ensemble learning techniques to improve forecasting accuracy. The work by Gonzales et al. [1] introduces a novel time series ensemble approach for analyzing and forecasting electricity prices in the Peruvian market. Similarly, Divina et al. [2] employ a stacking ensemble learning method to predict Iberian pig activity, highlighting the versatility and effectiveness of ensemble techniques in diverse applications. Another prominent topic is the integration of neural networks with other sophisticated models to enhance prediction capabilities. Ni et al. [3] present an optimized gated recurrent unit-temporal convolutional network for flood prediction, complemented by improved kernel density estimation for error assessment. This hybrid approach demonstrates the potential of combining different neural network architectures to tackle complex forecasting challenges. Rojas et al. [4] further this exploration by proposing the EMDFormer model, which integrates recurrent neural networks and transformers for financial forecasting, emphasizing the evolving landscape of neural network-based models. The forecasting of population dynamics and its implications are also well-represented. Li et al. [5] apply a NAR neural network Markov model to predict population trends in Huizhou and Guangdong, showcasing the application of neural networks in demographic studies. Chen et al. [6] explore 24164 AIMS Mathematics Volume 9, Issue 9, 24163–24165. optimizing a tourism revenue prediction model using the grey Markov chain, offering valuable insights into the economic impact of tourism in Macau. Healthcare and safety applications of time series forecasting are highlighted in the study by Alrashidi et al. [7], who predict hospital disposition for trauma patients using data-driven machine learning algorithms. This research underscores the critical role of forecasting in improving healthcare outcomes and resource management. Detecting anomalies and intrusions in data streams is another crucial area covered in this issue. Chu et al. [8] focus on intrusion detection in IoT data streams using concept drift localization, providing a robust framework for maintaining security in rapidly changing data environments. Finally, the theoretical advancements in time series analysis are addressed by Aly et al. [9], who explore generalized inequalities on diamond alpha time scales, contributing to the mathematical foundations of time series forecasting. In conclusion, this special issue presents diverse studies that collectively push the boundaries of time series forecasting. The innovative methodologies and applications featured herein not only enhance our understanding of forecasting techniques but also pave the way for future research and practical implementations in various fields. Guest Editors Professor Francisco Martínez-Álvarez, Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain Email: [email protected]. Professor Jose. F. Torres, Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain Email: [email protected]. Professor María Martínez-Ballesteros, Department of Computer Science, University of Seville, ES-41012 Seville, Spain Email: [email protected]. Professor Alicia Troncoso, Data Science and Big Data Lab, Pablo de Olavide University, ES-41013 Seville, Spain Email: [email protected]. Conflict of interest The authors declare no conflicts of interest. References 1. S. M. Gonzales, H. Iftikhar, J. Linkolk Lopez-Gonzales, Analysis and forecasting of electricity prices using an improved time series ensemble approach: an application to the Peruvian electricity market, AIMS Math., 9 (2024), 21952–21971. https://doi.org/10.3934/math.20241067 2. F. Divina, M. Garcia-Torres, F. Gomez-Vela, D. S. Rodriguez-Baena, A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach, AIMS Math., 9 (2024), 13358–13384. https://doi.org/10.3934/math.2024652 24165 AIMS Mathematics Volume 9, Issue 9, 24163–24165. 3. C. Ni, M. F. Marsani, F. P. Shan, X. Zou, Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation, AIMS Math., 9 (2024), 14681–14696. https://doi.org/10.3934/math.2024714 4. A. L. de Rojas, M. A. Jaramillo-Moran, J. E. Sandubete, EMDFormer model for time series forecasting, AIMS Math., 9 (2024), 9419–9434. https://doi.org/10.3934/math.2024459 5. D. Li, M. Qiu, Z. Luo, Huizhou resident population, Guangdong resident population and elderly population forecast based on the NAR neural network Markov model, AIMS Math., 9(2024), 3235–3252. https://doi.org/10.3934/math.2024157 6. X. Chen, H. Zhan, C. U. I. Wong, Optimization study of tourism total revenue prediction model based on the Grey Markov chain: a case study of Macau, AIMS Math., 9 (2024), 16187–16202. https://doi.org/10.3934/math.2024783 7. N. Alrashidi, M. Alrashidi, S. Mejahed, A. A. Eltahawi, Predicting hospital disposition for trauma patients: application of data-driven machine learning algorithms, AIMS Math., 9 (2024), 7751–7769. https://doi.org/10.3934/math.2024376 8. R. Chu, P. Jin, H. Qiao, Q. Feng, Intrusion detection in the IoT data streams using concept drift localization, AIMS Math., 9 (2024), 1535–1561. https://doi.org/10.3934/math.2024076 9. E. S. Aly, A. M. Mahnashi, A. A. Zaagan, I. Ibedou, A. I. Saied, W. W. Mohammed, N-dimension for dynamic generalized inequalities of Holder and Minkowski type on diamond alpha time scales, AIMS Math., 9 (2024), 9329–9347. https://doi.org/10.3934/math.2024454 © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)