Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models

Excerpt

arXiv:2511.00053v2 Announce Type: replace Abstract: The design of learning objectives is central to training time-series forecasting models. Existing learning objectives such as mean squared error mostly treat each future step as an independent, equally weighted task, which leads to the following two challenges: (1) they overlook the label autocorrelation effect among future steps, leading to biased learning objectives; (2) they fail to set heterogeneous task weights for different forecasting ta