Semantics-Enhanced Retrieval-Augmented 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:2603.22372v1 Announce Type: cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time

arXiv:2607.06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure

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: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:2608.23855v1 Announce Type: new Abstract: We propose ICI-Time, a novel framework that reframes time series forecasting as a visual inpainting task, leveraging the

<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

arXiv:2604.05468v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction