Quantile Forecast

Quantile forecasting aims to predict not just the most likely value of a future outcome (like a point forecast), but the entire probability distribution, focusing on specific quantiles (e.g., the 10th, 50th, and 90th percentiles). Current research emphasizes improving the accuracy and calibration of these quantile forecasts, particularly for complex time series data, using diverse models such as quantile regression forests, neural networks (including neural processes), and ensembles of heterogeneous models. This focus is driven by the need for more robust uncertainty quantification in various applications, including electricity demand forecasting, communication systems, and financial modeling, where accurate probabilistic predictions are crucial for risk management and decision-making.