Value Prediction

Value prediction, the task of estimating future values based on current observations, is a core problem across diverse fields, aiming to improve decision-making and forecasting accuracy. Current research emphasizes the application of machine learning, particularly deep learning architectures like recurrent and convolutional neural networks, ensemble methods (e.g., gradient boosting), and extreme learning machines, to predict values in various domains, from gait analysis to market pricing and compiler optimization. These advancements improve prediction accuracy and efficiency, leading to tangible benefits in areas such as healthcare monitoring, sports analytics, and resource management. However, challenges remain in addressing issues like data limitations, model interpretability, and the reliability of surrogate objectives used in training.