标题： DeepSupp：基于注意力驱动的相关模式分析的动态时间序列支撑和阻力水平识别 显示英文标题

标题： DeepSupp: Attention-Driven Correlation Pattern Analysis for Dynamic Time Series Support and Resistance Levels Identification

摘要： 支撑和阻力（SR）水平是技术分析的核心，指导交易者进行入场、出场和风险管理。 尽管被广泛使用，传统的SR识别方法往往难以适应现代波动性市场的复杂性。 最近的研究引入了机器学习技术来解决以下挑战，但大多数研究集中在价格预测而非结构水平识别上。 本文介绍了 DeepSupp，一种新的深度学习方法，用于检测金融支撑水平，利用多头注意力机制分析空间相关性和市场微观结构关系。 DeepSupp结合了先进的特征工程，构建动态相关矩阵以捕捉不断变化的市场关系，并采用基于注意力的自编码器进行鲁棒的表示学习。 最终的支撑水平通过无监督聚类提取，利用DBSCAN识别重要的价格阈值。 在标普500股票代码上的全面评估表明， DeepSupp优于六种基线方法，在六个金融指标上达到最先进的性能，包括基本的支撑准确性以及市场制度敏感性。 在各种市场条件下表现出一致的结果，DeepSupp解决了SR水平检测中的关键缺口，为现代金融分析提供了可扩展且可靠解决方案。 我们的方法突显了基于注意力的架构在揭示细微市场模式和改进技术交易策略方面的潜力。 显示英文摘要

摘要： Support and resistance (SR) levels are central to technical analysis, guiding traders in entry, exit, and risk management. Despite widespread use, traditional SR identification methods often fail to adapt to the complexities of modern, volatile markets. Recent research has introduced machine learning techniques to address the following challenges, yet most focus on price prediction rather than structural level identification. This paper presents DeepSupp, a new deep learning approach for detecting financial support levels using multi-head attention mechanisms to analyze spatial correlations and market microstructure relationships. DeepSupp integrates advanced feature engineering, constructing dynamic correlation matrices that capture evolving market relationships, and employs an attention-based autoencoder for robust representation learning. The final support levels are extracted through unsupervised clustering, leveraging DBSCAN to identify significant price thresholds. Comprehensive evaluations on S&P 500 tickers demonstrate that DeepSupp outperforms six baseline methods, achieving state-of-the-art performance across six financial metrics, including essential support accuracy and market regime sensitivity. With consistent results across diverse market conditions, DeepSupp addresses critical gaps in SR level detection, offering a scalable and reliable solution for modern financial analysis. Our approach highlights the potential of attention-based architectures to uncover nuanced market patterns and improve technical trading strategies.