Forecasting Task

Time series forecasting aims to predict future values based on historical data, a crucial task across diverse fields like healthcare, finance, and transportation. Current research emphasizes improving accuracy and efficiency through advanced model architectures, including neural networks (recurrent, transformer-based, and graph neural networks), Bayesian methods, and parameter-efficient fine-tuning techniques. These advancements address challenges like handling missing data, adapting to non-stationary data, and incorporating multiple data sources for more robust and accurate predictions, ultimately impacting decision-making in various sectors.