Research on Olympic Medal Distribution Prediction Based on Exponential Smoothing, Frequency Probability, and Logistic Regression Models

This paper proposes the Exponential Smoothing Model, Frequency Probability Model, and Logistic Regression Model, focusing on the prediction methods of multiple models in scenarios with varying data characteristics. First, for scenarios with large datasets or stable trends, the Exponential Smoothing Model is introduced. It captures trend changes through weighted averages of historical data (with higher weights assigned to more recent data) and, through cross-validation, determines the parameters to predict future total and categorized quantities of the target object while analyzing their changing trends. Next, for scenarios with smaller datasets or larger fluctuations, the Frequency Probability Model is used, estimating future probabilities based on the frequency of historical events. By comparing data at different stages of typical cases, the model quantifies the impact of specific factors on increasing the probability of an event occurring. Finally, for zero-sample scenarios, the Logistic Regression Model is constructed, incorporating variables such as participant scale and historical participation frequency. Adaptive optimization algorithms are used to solve the parameters and predict the probability of first-time events, identifying high-potential objects and fields, and providing recommendations for resource allocation. The Exponential Smoothing Model is effective for predicting stable data, the Frequency Probability Model quantifies the probability characteristics of repeatable events, and the Logistic Regression Model provides a multi-factor probability prediction framework for zero-sample scenarios. Together, the three models offer diverse predictive approaches for different data characteristic scenarios.