Hybrid Prediction Model

Hybrid prediction models combine different machine learning techniques to improve predictive accuracy and robustness, addressing limitations of individual methods. Current research focuses on integrating neural networks (like LSTMs and Set Transformers) with rule-based systems or traditional statistical models, often incorporating techniques like clustering and feature selection to optimize performance for specific applications, such as trajectory imputation and time series forecasting. These advancements are impacting diverse fields, from energy forecasting and data management to sports analytics and medical diagnostics, by enabling more accurate and reliable predictions from complex, often incomplete datasets.