has been a shift in how foundation models operate. After establishing that pretraining a large deep learning model on a vast corpus of temporal data grants generalizable properties, pretrained TS models now aim to be even more versatile. For time series, this means supporting exogenous variables and allowing variable context and prediction lengths. This article discusses  Timer-XL[1] , an upgraded time series model based on  Timer[2] . Timer-XL is built for generalization, with a focus on long-context forecasting. Let’s get started! What is Timer-XL Timer-XL is a decoder-only Transformer foundation model for forecasting. The model emphasizes generalizability and long-context predictions — offering unified, long-range forecasting.…