Application of the Kalman filter method in data mining in stochastic modeling courses

Abstract

This article presents the successful application of the Kalman Filter method in two different contexts: the estimation of daily cases of COVID-19 in Colombia and the projection of points for two soccer teams in the Spanish Professional League.

In the first case, the complexity of predicting the evolution of the pandemic in a diverse country like Colombia is addressed. The Kalman Filter proved to be a robust tool to adjust and improve daily case estimates, providing more accurate results than conventional methods. The filter's ability to adapt to changes in epidemiological trends is revealed as a crucial factor in the effective management of the health crisis.

In the second case, the focus shifts to the sports field, specifically the Spanish Professional Football League. The application of the Kalman Filter to project the points obtained by two teams reveals its usefulness in the analysis of performance throughout the season. The results suggest that this method can be a valuable tool for coaches and analysts, allowing them to adjust strategies in real time and anticipate possible fluctuations in performance.

In both cases, the versatility of the Kalman Filter emerges as a crucial factor, highlighting its ability to adapt to different types of data and contexts. These findings underline the potential of this technique not only in the management of health crises, but also in strategic decision-making in the field of sports, consolidating its position as a valuable analytical tool in various disciplines.