Evaluating Teacher Forcing and Curriculum Learning in Recurrent Models for Oceanographic Time Series - Marum, Tiago H.; Agra, Ronney; Barros, Marcel Rodrigues de; Costa, Anna Helena Reali; Cozman, Fábio Gagliardi; Lofrano, Fábio Cunha; Kurokawa, Fernando Akira (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Sea level prediction is vital for port and coastal operations, where short-term forecasting accuracy is critical for navigation and planning. Autoregressive neural models are powerful tools for this task, especially when combined with training strategies like Teacher Forcing and Curriculum Learning. This study analyzes their impact on RNN performance for short-term sea level forecasting using high-frequency data from the Port of Santos, Brazil. We compare standard autoregressive training, Teacher Forcing, and Curriculum Learning across MAE, RMSE, and R² metrics, focusing on long prediction windows. Results show that low levels of teacher forcing improve convergence and reduce error over long horizons, highlighting its value for precision and long-term stability.