The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Fore
arXiv:2604.05364v1 Announce Type: new Abstract: We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally,
<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
arXiv:2610.02608v1 Announce Type: new Abstract: Time series forecasting (TSF) increasingly drives decisions in transportation, energy, finance, healthcare, and infrastructure, yet
<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: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: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
arXiv:2604.05064v1 Announce Type: cross Abstract: Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static
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