Probabilistic Time Series Forecasting

Probabilistic time series forecasting aims to predict future values of a time series along with a measure of uncertainty, providing more informative predictions than traditional point forecasts. Recent research heavily emphasizes generative models, such as diffusion models and normalizing flows, often combined with recurrent neural networks (RNNs) or transformers, to capture complex temporal dependencies and generate accurate probability distributions. These advancements are improving forecasting accuracy and uncertainty quantification across diverse applications, from renewable energy prediction to financial modeling, and are driving the development of more efficient and robust methods, including autoML approaches. The field is also actively exploring improved evaluation metrics and techniques for handling missing data and high-dimensional datasets.