Author(s)

JinXing Lu

Affiliation(s)

School of Mathematical Science, Yangzhou University, Yangzhou 225002, Jiangsu, China.

Corresponding Author

JinXing Lu

ABSTRACT

This paper proposes an innovative TPE-Seq2Seq model for Olympic medal prediction by integrating sequence-to-sequence deep learning with Tree-structured Parzen Estimator hyperparameter optimization. Utilizing historical Olympic data from the International Olympic Committee, we first constructed a comprehensive dataset through BP Neural Network-based imputation of missing values and integration of non-medal-winning nations. The model captures complex temporal patterns and feature relationships through an encoder-decoder architecture, with key hyperparameters (learning rate, hidden units, regularization coefficients) systematically optimized via TPE to mitigate overfitting and enhance generalization. Experimental results demonstrate significant performance improvements, achieving an R2 value of 0.875 on the test set. Monte Carlo simulation and 95% confidence intervals quantify prediction uncertainty, revealing stable forecasts for six leading nations at the 2028 Los Angeles Olympics. Notably, the model predicts 41 gold medals for the United States and 40 for China, with narrow confidence intervals (e.g., US gold: [39,42]), demonstrating high reliability. This data-driven framework offers strategic insights for national Olympic committees and event organizers in resource allocation and competition planning.

KEYWORDS

Olympic medal prediction; TPE-Seq2Seq model; Hyperparameter optimization; Confidence interval

CITE THIS PAPER

JinXing Lu. Olympic medal prediction based on TPE-Seq2Seq model. World Journal of Information Technology. 2025, 3(3): 41-46. DOI: https://doi.org/10.61784/wjit3041.

REFERENCES

[1] Zhao Tingting, Chen Yuning. The application of Multivariate Statistical Analysis in the economic benefits of Starbucks stores in Xi′an. Pure and Applied Mathematics, 2024, 40(02): 301-310.

[2] Sajini K, Desgranges C, Delhommelle J. Advancing the design of gold nanomaterials with machine-learned potentials. Nano Express, 2025, 6(2): 022001.

[3] Lokesh T K, A L L. Attentive Sequence-to-Sequence Modeling of Stroke Gestures Articulation Performance. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2021, 51(6): 663-672.

[4] Cihan P. Bayesian Hyperparameter Optimization of Machine Learning Models for Predicting Biomass Gasification Gases. Applied Sciences, 2025, 15(3): 1018.

[5] Zhang H, Li W, Wang G, et al. Predicting stomatal conductance of chili peppers using TPE-optimized LightGBM and SHAP feature analysis based on UAVs’ hyperspectral, thermal infrared imagery, and meteorological data. Computers and Electronics in Agriculture, 2025, 23, 1110036.

[6] Pan X, Wang H, Lei M, et al. A method for filling missing values in multivariate sequence bidirectional recurrent neural networks based on feature correlations. Journal of Computational Science, 2024, 83, 102472.

[7] Andrade D F E F, Jorge N L, DaSilva J C. Berezinskii-Kosterlitz-Thouless transition in the XY model on the honeycomb lattice: a comprehensive Monte Carlo analysis. Physica Scripta, 2025, 100(6): 065953.

[8] Golovko V V. Improving confidence intervals and central value estimation in small datasets through hybrid parametric bootstrapping. Information Sciences, 2025, 716, 122254.