Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testing

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

arXiv:2608.17299v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting.

arXiv:2604.22328v1 Announce Type: cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for

<!-- SC_OFF --><div class="md"><p>Most of the content about TSFMs:</p> <ul> <li>Either overhypes their true potential,</li> <li>Or highlights weaknesses that are either irrelevant

<p>Most teams trust their historical data for good reason. It records what actually happened. It reflects real customers, real markets, and real operating conditions. When it is

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

arXiv:2604.18751v1 Announce Type: cross Abstract: Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the

arXiv:2607.13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions. Despite