Forecasting Method

Time series forecasting aims to predict future values based on historical data, a crucial task across diverse fields like finance and energy. Current research emphasizes improving forecast accuracy and robustness, particularly focusing on advanced machine learning models such as Graph Neural Networks (GNNs), Long Short-Term Memory networks (LSTMs), and Gaussian Processes (GPs), as well as hybrid approaches combining statistical and machine learning methods. A significant trend involves automating model selection and pipeline optimization to handle the increasing complexity and volume of time series data, leading to more efficient and reliable forecasting systems. These advancements have substantial implications for various industries, enabling better resource allocation, risk management, and decision-making.