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.

168,694 papers · 148 categories

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4 results for “CNN-GRU”

From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clusteringcs.LG

Paper proposes a novel approach to improve temporal clustering of time series data.

problem Challenges in clustering temporal data with varying sampling rates and high dimensionality.

method Transform time series into Euclidean space using similarity measures, then use CNN-GRU autoencoder for latent representation.

result Approach outperforms existing methods by up to 32% on various time series datasets.

temporal clustering time series CNN-GRU autoencoder clustering algorithms

2020-10-02abs ↗pdf ↗

Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systemscs.LG

Integrates CNN and GRU for precise stock market risk alerts.

problem Predicting future stock market risks and providing early warnings.

method Uses CNN for feature extraction and GRU for time series analysis.

result Effective early warnings of future stock market risks.

financial markets sentiment analysis CNN GRU risk prediction

2024-12-13abs ↗pdf ↗

AlphaZeroBeta: Deep Reinforcement Learning for Market-Neutral Portfoliosq-fin.PM

AlphaZeroBeta uses deep reinforcement learning for market-neutral portfolios, outperforming traditional methods.

problem Traditional portfolio management methods often fail during market regime shifts or when assumptions break down.

method Combines a composite reward function and CNN-GRU policy trained end-to-end via Recurrent PPO.

result Achieves higher Sharpe ratios than baselines while maintaining near-zero benchmark correlations.

deep reinforcement learning market-neutral portfolios reinforcement learning portfolio management deep learning

2026-07-20abs ↗pdf ↗

A Hierarchical Deep Convolutional Neural Network and Gated Recurrent Unit Framework for Structural Damage Detectioneess.SP

A new framework combines CNN and GRU for better structural damage detection.

problem Improving damage detection in structural engineering using machine learning.

method Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model spatial and temporal relations.

result The proposed HCG framework significantly outperforms existing methods for structural damage detection.

structural damage detection deep learning CNN GRU machine learning

2020-05-29abs ↗pdf ↗