On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

4 results for “teleconnections”

Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecastingstat.ML

TelePiT improves S2S forecasting by integrating physics and teleconnections.

problem Challenges in subseasonal-to-seasonal climate forecasting due to chaotic dynamics and complex interactions.

method Integrates physics and teleconnections into a transformer architecture with spherical embedding and multi-scale physics-informed neural ODE.

result Significantly outperforms state-of-the-art methods across all forecast horizons.

teleconnections physics-informed transformer S2S forecasting climate prediction

2025-06-08abs ↗pdf ↗

Data-driven Seasonal Climate Predictions via Variational Inference and Transformersphysics.ao-ph

Study combines variational inference and transformers for seasonal climate predictions.

problem Lack of robust seasonal predictions due to limited historical records and computational constraints.

method Combines variational inference with transformer models trained on climate model output.

result Method provides skilful predictions beyond climate change-induced trends in various regions.

climate predictions machine learning variational inference transformers seasonal anomalies

2025-03-26abs ↗pdf ↗

Graph Neural Networks for Improved El Niño Forecastingcs.LG

Graph Neural Networks improve El Niño forecasts.

problem Improving seasonal forecasting models for ENSO.

method Application of spatiotemporal Graph Neural Networks.

result Preliminary results outperform state-of-the-art systems for 1 and 3-month projections.

Graph Neural Networks El Niño Forecasting Spatiotemporal Models Seasonal Forecasting ENSO

2020-12-02abs ↗pdf ↗

Pitfalls of Climate Network Construction: A Statistical Perspectivecs.LG

Statistical analysis reveals pitfalls in climate network construction.

problem Uncertainty in correlation estimates affects network characteristics.

method Simulated isotropic random fields and resampling procedures.

result Spurious link bundles and high-degree clusters are common.

climate networks statistical perspective uncertainty spurious correlations resampling

2022-11-05abs ↗pdf ↗