Editorial Special Issues

Advances in time series forecasting: innovative methods and applications

  • Received: 07 August 2024 Revised: 07 August 2024 Accepted: 14 August 2024 Published: 14 August 2024
  • Citation: J. F. Torres, M. Martinez-Ballesteros, A. Troncoso, F. Martinez-Alvarez. Advances in time series forecasting: innovative methods and applications[J]. AIMS Mathematics, 2024, 9(9): 24163-24165. doi: 10.3934/math.20241174

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    [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 doi: 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 doi: 10.3934/math.2024652
    [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 doi: 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 doi: 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 doi: 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 doi: 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 doi: 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 doi: 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 doi: 10.3934/math.2024454
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  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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