In-Context Inpainting for Time Series Forecasting

arXiv:2604.05364v1 Announce Type: new Abstract: We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally,

arXiv:2607.20493v1 Announce Type: new Abstract: Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing.

arXiv:2606.14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent

arXiv:2610.24516v1 Announce Type: new Abstract: Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of

arXiv:2606.27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1x9n3qv/tempopfn_synthetic_pretraining_of_linear_rnns_for/"> <img alt="TempoPFN: Synthetic Pre-training of

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1x97v19/tempopfn_synthetic_pretraining_of_linear_rnns_for/"> <img alt="TempoPFN: Synthetic Pre-training