<?xml version="1.0" encoding="UTF-8"?>
<article xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="en" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <front>
    <journal-meta>
      <journal-id journal-id-type="ojs">JDF</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Journal of Digital Frontier</journal-title>
      </journal-title-group>
      <publisher>
        <publisher-name>Digital Intelligence Press</publisher-name>
        <publisher-loc>
          <country>HK</country>
          <uri>https://dipscie.com/index.php/JDF/index</uri>
        </publisher-loc>
      </publisher>
      <issn pub-type="epub">3135-7695</issn>
      <self-uri xlink:href="https://dipscie.com/index.php/JDF"/>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">12</article-id>
      <article-categories>
        <subj-group xml:lang="en" subj-group-type="heading">
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title xml:lang="en">&lt;bold&gt;Modeling of the Randomness in the Process of E-sports Competitions Based on Neural Stochastic Differential Equations&lt;/bold&gt;</article-title>
      </title-group>
      <contrib-group content-type="author">
        <contrib>
          <name-alternatives>
            <name name-style="western" specific-use="primary">
              <surname>Ma</surname>
              <given-names>Shuai</given-names>
            </name>
          </name-alternatives>
          <email>mashuai9248@163.com</email>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution content-type="orgname">College of Physical Education, Hanyang University, Seoul, Republic of Korea</institution>
      </aff>
      <pub-date date-type="pub" publication-format="epub">
        <day>09</day>
        <month>07</month>
        <year>2026</year>
      </pub-date>
      <fpage>1</fpage>
      <lpage>30</lpage>
      <pub-history>
        <event event-type="received">
          <event-desc>Received: <date date-type="received" iso-8601-date="2026-07-09T02:21:22+00:00"><day>9</day><month>7</month><year>2026</year></date></event-desc>
        </event>
      </pub-history>
      <permissions>
        <copyright-statement>Copyright (c) 2026 Shuai Ma (Author)</copyright-statement>
        <copyright-year>2026</copyright-year>
        <copyright-holder>Shuai Ma (Author)</copyright-holder>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0">
          <license-p>&lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;&lt;img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;This work is licensed under a &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;Creative Commons Attribution 4.0 International License&lt;/a&gt;.&lt;/p&gt;</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://dipscie.com/index.php/JDF/article/view/Vol1_No1_2026"/>
      <kwd-group xml:lang="en">
        <kwd>Neural Stochastic Differential Equations; Modeling of E-sports Competitions; Stochastic Process; Uncertainty Quantification; Multimodal Temporal Prediction</kwd>
      </kwd-group>
      <counts>
        <page-count count="30"/>
      </counts>
      <custom-meta-group/>
    </article-meta>
  </front>
  <body>
    <p>

  
    
      JDF
      
        Journal of Digital Frontier
      
      
        Digital Intelligence Press
        
          HK
          https://dipscie.com/index.php/JDF/index
        
      
      3135-7695
      
    
    
      12
      
        
          Articles
        
      
      
        &lt;bold&gt;Modeling of the Randomness in the Process of E-sports Competitions Based on Neural Stochastic Differential Equations&lt;/bold&gt;
      
      
        
          
            
              Ma
              Shuai
            
          
          mashuai9248@163.com
        
      
      
        
          Received: 972026
        
      
      
      
        Neural Stochastic Differential Equations; Modeling of E-sports Competitions; Stochastic Process; Uncertainty Quantification; Multimodal Temporal Prediction
      
      
    
  
  
  
    
      
        [1] Xiao, X. (2022). Developmental research on international competitiveness of E-Sports industry in China: A comparative study between China and South Korea. AuthorHouse.
      
      
        [2] Cheng, B., &amp; Bayasgalan, Z. (2024). RESEARCH ON THE DEVELOPMENT PROSPECT OF CONTEMPORARY E-SPORTS EDUCATION. In ICERI2024 Proceedings (pp. 1270-1277). IATED. https://doi.org/10.21125/iceri.2024.0391
      
      
        [3] Liu, F., She, Q., Qiu, J., &amp; Wei, Z. (2024). Theoretical logic and strategy of digital economy driving the high-quality development of China's regional E-sports industry. Journal of Management and Social Development (4), 11-15. https://doi.org/10.62517/jmsd.202412402
      
      
        [4] Pfoff, J. C., &amp; Lee, N. (2024). Super Smash Bros. Ultimate and E-sports. In Encyclopedia of Computer Graphics and Games (pp. 1783-1785). Cham: Springer International Publishing.
      
      
        https://doi.org/10.1007/978-3-031-23161-2_472
      
      
        [5] Vasiliev, A. A., &amp; Pechatnova, J. V. (2023). Regulatory models in e-sports. Legal Issues in the digital Age, (4), 4-22. https://doi.org/10.17323/2713-2749.2023.4.4.22
      
      
        [6] Bahrololloomi, F., Klonowski, F., Sauer, S., Horst, R., &amp; Dörner, R. (2023). E-sports player performance metrics for predicting the outcome of league of legends matches considering player roles. SN Computer Science, 4(3), 238. https://doi.org/10.1007/s42979-022-01660-6
      
      
        [7] Lu, Y., Chen, H., &amp; Yan, H. (2022). E‐Sports Competition Analysis Based on Intelligent Analysis System. Computational Intelligence and Neuroscience, 2022(1), 4855550. https://doi.org/10.1155/2022/4855550
      
      
        [8] Gerken, J., Zhang, H., Garnica Caparrós, M., Gardeweg, L., Memmert, D., &amp; Wunderlich, F. (2026). The issue of sparse networks in sports competitions: can Elo ratings efficiently compare football teams that never play a match?. Journal of the Operational Research Society, 1-16.
      
      
        https://doi.org/10.1080/01605682.2025.2612140
      
      
        [9] Jalovaara, P. (2024). Win probability estimation for strategic decision-making in esports.
      
      
        [10] Dai, M., Duan, J., Hu, J., Wen, J., &amp; Wang, X. (2022). Variational inference of the drift function for stochastic differential equations driven by Lévy processes. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(6). https://doi.org/10.48550/arXiv.2103.15080
      
      
        [11] Schurz, H. (2024). A brief review on stability investigations of numerical methods for systems of stochastic differential equations. Networks &amp; Heterogeneous Media, 19(1). https://doi.org/10.3934/nhm.2024016
      
      
        [12] Yang, C. F. (2025). Uncertainty-Aware QoS Forecasting with BR-LSTM for Esports Networks. Information, 16(12), 1016. https://doi.org/10.3390/info16121016
      
      
        [13] Liu, G., Luo, Y., Schulte, O., &amp; Poupart, P. (2022). Uncertainty-aware reinforcement learning for risk-sensitive player evaluation in sports game. Advances in Neural Information Processing Systems, 35, 20218-20231. https://doi.org/10.52202/068431-1470
      
      
        [14] Jalovaara, P. (2024). Win probability estimation for strategic decision-making in esports.
      
      
        [15] Sufian, M. A., Varadarajan, J., Hanumanthu, M., Katneni, L., Jamil, A., Lal, V., &amp; Boomer, J. (2024, June). Optimizing E-Sports Revenue: A Novel Data Driven Approach to Predicting Merchandise Sales Through Data Analytics and Machine Learning. In Science and Information Conference (pp. 522-567). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-62269-4_35
      
      
        [16] Wardaszko, M., &amp; Kriz, W. C. (2025, July). Modelling In-Game Events in Game Scenarios: A Comprehensive Framework. In International Simulation and Gaming Association Conference (pp. 63-78). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-20129-4_5
      
      
        [17] Ötting, M., &amp; Karlis, D. (2023). Football tracking data: a copula-based hidden Markov model for classification of tactics in football. Annals of Operations Research, 325(1), 167-183. https://doi.org/10.1007/s10479-022-04660-0
      
      
        [18] Kim, J., &amp; Chatterjea, R. (2026). Stochastic Differential Equation Treatment of OPS in Baseball.
      
      
        [19] Chulajata, K., Wu, S., Scalzo, F., &amp; Cha, E. S. (2024). Predicting outcomes in video games with long short term memory networks. arXiv preprint arXiv:2402.15923. https://doi.org/10.48550/arXiv.2402.15923
      
      
        [20] Xu, H., Lin, B., &amp; Liu, L. (2025). Sports event data analysis and win rate prediction model using self-attention mechanism and Transformer. Journal of Computational Methods in Sciences and Engineering, 14727978251348637. https://doi.org/10.1177/14727978251348637
      
      
        [21] Kovačević, M. A., Pešović, M. D., Petrović, Z. Z., &amp; Pucanović, Z. S. (2024). Predictive analytics of in-game transactions: tokenized player history and self-attention techniques. IEEE Access, 12, 149263-149271. https://doi.org/10.1109/access.2024.3477624
      
      
        [22] Montrucchio, M., Barbierato, E., &amp; Gatti, A. (2026). Uncertainty-Aware Machine Learning for NBA Forecasting in Digital Betting Markets. Information, 17(1), 56. https://doi.org/10.3390/info17010056
      
      
        [23] Liu, J., Zou, C., &amp; Chintagunta, P. K. (2026). Bayesian learning and skill accumulation in video game play. Quantitative Marketing and Economics, 24(1), 5. https://doi.org/10.1007/s11129-026-09309-x
      
      
        [24] Dong, S., Xi, G., &amp; Li, B. (2026). Intelligent Games for UAV Systems: A Survey of Game-Theoretic and AI-Enabled Methods. Journal of Artificial Intelligence and Technology. https://doi.org/10.37965/jait.2026.1237
      
      
        [25] Aslanimoghanloo, M., ElGazzar, A., &amp; van Gerven, M. (2025). Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations. arXiv preprint arXiv:2511.16427. https://doi.org/10.1016/j.jbi.2026.105043
      
      
        [26] Di Persio, L., Alruqimi, M., &amp; Garbelli, M. (2024). Stochastic approaches to energy markets: from stochastic differential equations to mean field games and neural network modeling. Energies, 17(23), 6106. https://doi.org/10.3390/en17236106
      
      
        [27] Oh, Y., Kam, S., Lee, J., Lim, D. Y., Kim, S., &amp; Bui, A. (2025). Comprehensive review of neural differential equations for time series analysis. arXiv preprint arXiv:2502.09885. https://doi.org/10.24963/ijcai.2025/1179
      
      
        [28] Eggen, S., Espe, T. J., Grude, K., Risstad, M., &amp; Sandberg, R. (2026). Financial time series uncertainty: A review of probabilistic AI applications. Journal of Economic Surveys, 40(2), 915-953. https://doi.org/10.1111/joes.70018
      
      
        [29] Jiang, S., Zhang, L., Xu, H., Huang, J., He, Q., Zhou, X., ... &amp; Jiang, J. (2024, October). GameTrail: Probabilistic Lifecycle Process Model for Deep Game Understanding. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 994-1003). https://doi.org/10.1145/3627673.3679736
      
      
        [30] Vardakis, M., Margetis, G., Chatzakis, I., Apostolakis, K. C., &amp; Stephanidis, C. (2026). Prediction of MOBA game events based on In-Game Data. Entertainment Computing, 101091. https://doi.org/10.1016/j.entcom.2026.101091
      
      
        [31] Ghasemifard, A. (2025). Milstein scheme for the numerical solution of first-order uncertain stochastic differential equations in stock price simulation. Caspian Journal of Mathematical Sciences, 14(2), 433. https://doi.org/10.22080/cjms.2025.29406.1763
      
      
        [32] Zhang, Q. (2026). Research on Multimodal Prediction Model of Sports Event Ticket Revenue Based on Transformer. International Scientific Technical and Economic Research, 4(2), 124-147. https://doi.org/10.71451/ISTAER2618
      
      
        [33] Zhou, H. (2026). Research on the Fusion Model of DeepFM and XGBoost for Digital Consumer Behavior Prediction. International Scientific Technical and Economic Research, 4(2), 98-123. https://doi.org/10.71451/ISTAER2617
      
      
        [34] Qin, Y. (2026). Research on Long Sequence Learning Behavior Modeling Based on Transformer-XL. International Scientific Technical and Economic Research, 4(2), 1-20. https://doi.org/10.71451/ISTAER2613
      
      
        [35] Xu, H. (2026). A Deep Reinforcement Learning Signal Control Algorithm for Traffic Carbon Emission Optimization. International Scientific Technical and Economic Research, 4(1), 200–221. https://doi.org/10.71451/ISTAER2610
      
      
        [36] Chen, G. (2026). Research on algorithm improvement of ARIMA-LSTM hybrid model in time series prediction of inflation rate. International Scientific Technical and Economic Research, 4(1), 90-122. https://doi.org/10.71451/ISTAER2605
      
      
        [37] Ding, Y., &amp; Chen, G. (2026). Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network. International Scientific Technical and Economic Research, 4(1), 70-89. https://doi.org/10.71451/ISTAER2604
      
      
        [38] Zeng, X. (2026). Cross-Border Trade Fraud Detection via Integrated Heterogeneous Graph Neural Network and XGBoost. International Scientific Technical and Economic Research, 4(1),47-69. https://doi.org/10.71451/ISTAER2603
      
      
        [39] Wang, W., Shen, S., &amp; Wang, Y. (2026). Forecasting Short-Term Export Volumes with Hybrid Models Integrating SARIMA with Attention-Based LSTM. International Scientific Technical and Economic Research, 4(1),1-22. https://doi.org/10.71451/ISTAER2601
      
    
  

</p>
  </body>
  <back>
    <ref-list>
      <ref id="R1">
        <mixed-citation>[1] Duan, P., Wang, X., Zhang, A. Y., &amp; Ji, B. (2023). Development environment of China’s e-sports industry. In Electronic sports industry in China: An overview (pp. 47-74). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-9288-9_3</mixed-citation>
      </ref>
      <ref id="R2">
        <mixed-citation>[2] Cheng, B., &amp; Bayasgalan, Z. (2024). RESEARCH ON THE DEVELOPMENT PROSPECT OF CONTEMPORARY E-SPORTS EDUCATION. In ICERI2024 Proceedings (pp. 1270-1277). IATED. https://doi.org/10.21125/iceri.2024.0391</mixed-citation>
      </ref>
      <ref id="R3">
        <mixed-citation>[3] Liu, F., She, Q., Qiu, J., &amp; Wei, Z. (2024). Theoretical logic and strategy of digital economy driving the high-quality development of China's regional E-sports industry. Journal of Management and Social Development (4), 11-15. https://doi.org/10.62517/jmsd.202412402</mixed-citation>
      </ref>
      <ref id="R4">
        <mixed-citation>[4] Pfoff, J. C., &amp; Lee, N. (2024). Super Smash Bros. Ultimate and E-sports. In Encyclopedia of Computer Graphics and Games (pp. 1783-1785). Cham: Springer International Publishing.</mixed-citation>
      </ref>
      <ref id="R5">
        <mixed-citation>https://doi.org/10.1007/978-3-031-23161-2_472</mixed-citation>
      </ref>
      <ref id="R6">
        <mixed-citation>[5] Vasiliev, A. A., &amp; Pechatnova, J. V. (2023). Regulatory models in e-sports. Legal Issues in the digital Age, (4), 4-22. https://doi.org/10.17323/2713-2749.2023.4.4.22</mixed-citation>
      </ref>
      <ref id="R7">
        <mixed-citation>[6] Bahrololloomi, F., Klonowski, F., Sauer, S., Horst, R., &amp; Dörner, R. (2023). E-sports player performance metrics for predicting the outcome of league of legends matches considering player roles. SN Computer Science, 4(3), 238. https://doi.org/10.1007/s42979-022-01660-6</mixed-citation>
      </ref>
      <ref id="R8">
        <mixed-citation>[7] Lu, Y., Chen, H., &amp; Yan, H. (2022). E‐Sports Competition Analysis Based on Intelligent Analysis System. Computational Intelligence and Neuroscience, 2022(1), 4855550. https://doi.org/10.1155/2022/4855550</mixed-citation>
      </ref>
      <ref id="R9">
        <mixed-citation>[8] Gerken, J., Zhang, H., Garnica Caparrós, M., Gardeweg, L., Memmert, D., &amp; Wunderlich, F. (2026). The issue of sparse networks in sports competitions: can Elo ratings efficiently compare football teams that never play a match?. Journal of the Operational Research Society, 1-16.</mixed-citation>
      </ref>
      <ref id="R10">
        <mixed-citation>https://doi.org/10.1080/01605682.2025.2612140</mixed-citation>
      </ref>
      <ref id="R11">
        <mixed-citation>[9] Jalovaara, P. (2024). Win probability estimation for strategic decision-making in esports. https://urn.fi/URN:NBN:fi:aalto-202411217333</mixed-citation>
      </ref>
      <ref id="R12">
        <mixed-citation>[10] Dai, M., Duan, J., Hu, J., Wen, J., &amp; Wang, X. (2022). Variational inference of the drift function for stochastic differential equations driven by Lévy processes. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(6). https://doi.org/10.48550/arXiv.2103.15080</mixed-citation>
      </ref>
      <ref id="R13">
        <mixed-citation>[11] Schurz, H. (2024). A brief review on stability investigations of numerical methods for systems of stochastic differential equations. Networks &amp; Heterogeneous Media, 19(1). https://doi.org/10.3934/nhm.2024016</mixed-citation>
      </ref>
      <ref id="R14">
        <mixed-citation>[12] Yang, C. F. (2025). Uncertainty-Aware QoS Forecasting with BR-LSTM for Esports Networks. Information, 16(12), 1016. https://doi.org/10.3390/info16121016</mixed-citation>
      </ref>
      <ref id="R15">
        <mixed-citation>[13] Liu, G., Luo, Y., Schulte, O., &amp; Poupart, P. (2022). Uncertainty-aware reinforcement learning for risk-sensitive player evaluation in sports game. Advances in Neural Information Processing Systems, 35, 20218-20231. https://doi.org/10.52202/068431-1470</mixed-citation>
      </ref>
      <ref id="R16">
        <mixed-citation>[14] Zhou, J., Petrosian, O., &amp; Gao, H. (2024). Enhancing ecological uncertainty predictions in pollution control games through dynamic Bayesian updating. Scientific Reports, 14(1), 12594. https://doi.org/10.1038/s41598-024-63234-1</mixed-citation>
      </ref>
      <ref id="R17">
        <mixed-citation>[15] Sufian, M. A., Varadarajan, J., Hanumanthu, M., Katneni, L., Jamil, A., Lal, V., &amp; Boomer, J. (2024, June). Optimizing E-Sports Revenue: A Novel Data Driven Approach to Predicting Merchandise Sales Through Data Analytics and Machine Learning. In Science and Information Conference (pp. 522-567). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-62269-4_35</mixed-citation>
      </ref>
      <ref id="R18">
        <mixed-citation>[16] Wardaszko, M., &amp; Kriz, W. C. (2025, July). Modelling In-Game Events in Game Scenarios: A Comprehensive Framework. In International Simulation and Gaming Association Conference (pp. 63-78). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-20129-4_5</mixed-citation>
      </ref>
      <ref id="R19">
        <mixed-citation>[17] Ötting, M., &amp; Karlis, D. (2023). Football tracking data: a copula-based hidden Markov model for classification of tactics in football. Annals of Operations Research, 325(1), 167-183. https://doi.org/10.1007/s10479-022-04660-0</mixed-citation>
      </ref>
      <ref id="R20">
        <mixed-citation>[18] Kim, J., &amp; Chatterjea, R. (2026). Stochastic Differential Equation Treatment of OPS in Baseball.</mixed-citation>
      </ref>
      <ref id="R21">
        <mixed-citation>[19] Chulajata, K., Wu, S., Scalzo, F., &amp; Cha, E. S. (2024). Predicting outcomes in video games with long short term memory networks. arXiv preprint arXiv:2402.15923. https://doi.org/10.48550/arXiv.2402.15923</mixed-citation>
      </ref>
      <ref id="R22">
        <mixed-citation>[20] Xu, H., Lin, B., &amp; Liu, L. (2025). Sports event data analysis and win rate prediction model using self-attention mechanism and Transformer. Journal of Computational Methods in Sciences and Engineering, 14727978251348637. https://doi.org/10.1177/14727978251348637</mixed-citation>
      </ref>
      <ref id="R23">
        <mixed-citation>[21] Kovačević, M. A., Pešović, M. D., Petrović, Z. Z., &amp; Pucanović, Z. S. (2024). Predictive analytics of in-game transactions: tokenized player history and self-attention techniques. IEEE Access, 12, 149263-149271. https://doi.org/10.1109/access.2024.3477624</mixed-citation>
      </ref>
      <ref id="R24">
        <mixed-citation>[22] Montrucchio, M., Barbierato, E., &amp; Gatti, A. (2026). Uncertainty-Aware Machine Learning for NBA Forecasting in Digital Betting Markets. Information, 17(1), 56. https://doi.org/10.3390/info17010056</mixed-citation>
      </ref>
      <ref id="R25">
        <mixed-citation>[23] Liu, J., Zou, C., &amp; Chintagunta, P. K. (2026). Bayesian learning and skill accumulation in video game play. Quantitative Marketing and Economics, 24(1), 5. https://doi.org/10.1007/s11129-026-09309-x</mixed-citation>
      </ref>
      <ref id="R26">
        <mixed-citation>[24] Dong, S., Xi, G., &amp; Li, B. (2026). Intelligent Games for UAV Systems: A Survey of Game-Theoretic and AI-Enabled Methods. Journal of Artificial Intelligence and Technology. https://doi.org/10.37965/jait.2026.1237</mixed-citation>
      </ref>
      <ref id="R27">
        <mixed-citation>[25] Aslanimoghanloo, M., ElGazzar, A., &amp; van Gerven, M. (2025). Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations. arXiv preprint arXiv:2511.16427. https://doi.org/10.1016/j.jbi.2026.105043</mixed-citation>
      </ref>
      <ref id="R28">
        <mixed-citation>[26] Di Persio, L., Alruqimi, M., &amp; Garbelli, M. (2024). Stochastic approaches to energy markets: from stochastic differential equations to mean field games and neural network modeling. Energies, 17(23), 6106. https://doi.org/10.3390/en17236106</mixed-citation>
      </ref>
      <ref id="R29">
        <mixed-citation>[27] Oh, Y., Kam, S., Lee, J., Lim, D. Y., Kim, S., &amp; Bui, A. (2025). Comprehensive review of neural differential equations for time series analysis. arXiv preprint arXiv:2502.09885. https://doi.org/10.24963/ijcai.2025/1179</mixed-citation>
      </ref>
      <ref id="R30">
        <mixed-citation>[28] Eggen, S., Espe, T. J., Grude, K., Risstad, M., &amp; Sandberg, R. (2026). Financial time series uncertainty: A review of probabilistic AI applications. Journal of Economic Surveys, 40(2), 915-953. https://doi.org/10.1111/joes.70018</mixed-citation>
      </ref>
      <ref id="R31">
        <mixed-citation>[29] Jiang, S., Zhang, L., Xu, H., Huang, J., He, Q., Zhou, X., ... &amp; Jiang, J. (2024, October). GameTrail: Probabilistic Lifecycle Process Model for Deep Game Understanding. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 994-1003). https://doi.org/10.1145/3627673.3679736</mixed-citation>
      </ref>
      <ref id="R32">
        <mixed-citation>[30] Vardakis, M., Margetis, G., Chatzakis, I., Apostolakis, K. C., &amp; Stephanidis, C. (2026). Prediction of MOBA game events based on In-Game Data. Entertainment Computing, 101091. https://doi.org/10.1016/j.entcom.2026.101091</mixed-citation>
      </ref>
      <ref id="R33">
        <mixed-citation>[31] Ghasemifard, A. (2025). Milstein scheme for the numerical solution of first-order uncertain stochastic differential equations in stock price simulation. Caspian Journal of Mathematical Sciences, 14(2), 433. https://doi.org/10.22080/cjms.2025.29406.1763</mixed-citation>
      </ref>
      <ref id="R34">
        <mixed-citation>[32] Zhang, Q. (2026). Research on Multimodal Prediction Model of Sports Event Ticket Revenue Based on Transformer. International Scientific Technical and Economic Research, 4(2), 124-147. https://doi.org/10.71451/ISTAER2618</mixed-citation>
      </ref>
      <ref id="R35">
        <mixed-citation>[33] Zhou, H. (2026). Research on the Fusion Model of DeepFM and XGBoost for Digital Consumer Behavior Prediction. International Scientific Technical and Economic Research, 4(2), 98-123. https://doi.org/10.71451/ISTAER2617</mixed-citation>
      </ref>
      <ref id="R36">
        <mixed-citation>[34] Qin, Y. (2026). Research on Long Sequence Learning Behavior Modeling Based on Transformer-XL. International Scientific Technical and Economic Research, 4(2), 1-20. https://doi.org/10.71451/ISTAER2613</mixed-citation>
      </ref>
      <ref id="R37">
        <mixed-citation>[35] Xu, H. (2026). A Deep Reinforcement Learning Signal Control Algorithm for Traffic Carbon Emission Optimization. International Scientific Technical and Economic Research, 4(1), 200–221. https://doi.org/10.71451/ISTAER2610</mixed-citation>
      </ref>
      <ref id="R38">
        <mixed-citation>[36] Chen, G. (2026). Research on algorithm improvement of ARIMA-LSTM hybrid model in time series prediction of inflation rate. International Scientific Technical and Economic Research, 4(1), 90-122. https://doi.org/10.71451/ISTAER2605</mixed-citation>
      </ref>
      <ref id="R39">
        <mixed-citation>[37] Ding, Y., &amp; Chen, G. (2026). Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network. International Scientific Technical and Economic Research, 4(1), 70-89. https://doi.org/10.71451/ISTAER2604</mixed-citation>
      </ref>
      <ref id="R40">
        <mixed-citation>[38] Zeng, X. (2026). Cross-Border Trade Fraud Detection via Integrated Heterogeneous Graph Neural Network and XGBoost. International Scientific Technical and Economic Research, 4(1),47-69. https://doi.org/10.71451/ISTAER2603</mixed-citation>
      </ref>
      <ref id="R41">
        <mixed-citation>[39] Wang, W., Shen, S., &amp; Wang, Y. (2026). Forecasting Short-Term Export Volumes with Hybrid Models Integrating SARIMA with Attention-Based LSTM. International Scientific Technical and Economic Research, 4(1),1-22. https://doi.org/10.71451/ISTAER2601</mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>
