一种用于金融时间序列异常检测与风险预测的基于FEDformer的混合框架
A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series
AI总结：
针对传统深度学习模型难以捕捉非平稳金融数据长期依赖与周期模式的问题，提出集成FEDformer、残差异常检测器与风险预测头的混合框架，在多类金融数据集上取得更优的异常检测与风险预测效果。
摘要：
金融市场本身具有波动性，容易出现市场崩盘、闪崩、流动性危机等突发扰动，因此对金融时间序列开展精准的异常检测与早期风险预测，对于防范系统性不稳定、支撑理性投资决策至关重要。LSTM、GRU等传统深度学习模型往往难以捕捉高度非平稳金融数据中的长期依赖关系与复杂周期模式。为解决这一局限，本研究提出一种用于金融时间序列异常检测与风险预测的基于FEDformer的混合框架，该框架将频率增强分解Transformer（Frequency Enhanced Decomposed Transformer, FEDformer）与基于残差的异常检测器、风险预测头相集成。其中FEDformer模块在时域和频域同时对时间动态进行建模，将信号分解为趋势分量与季节分量以提升可解释性；基于残差的检测器通过分析预测误差识别异常波动，风险预测头则利用学习到的潜在嵌入预测潜在金融风险。在标普500、纳斯达克综合指数、布伦特原油2000-2024年数据集上开展的实验表明，所提模型性能优于基准方法，RMSE降低15.7%，异常检测F1分数提升11.5%。这些结果证实了该模型捕捉金融波动的有效性，可支撑构建可靠的市场崩盘预测与风险管理预警系统。
英文摘要：
Financial markets are inherently volatile and prone to sudden disruptions such as market crashes, flash collapses, and liquidity crises. Accurate anomaly detection and early risk forecasting in financial time series are therefore crucial for preventing systemic instability and supporting informed investment decisions. Traditional deep learning models, such as LSTM and GRU, often fail to capture long-term dependencies and complex periodic patterns in highly nonstationary financial data. To address this limitation, this study proposes a FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series, which integrates the Frequency Enhanced Decomposed Transformer (FEDformer) with a residual-based anomaly detector and a risk forecasting head. The FEDformer module models temporal dynamics in both time and frequency domains, decomposing signals into trend and seasonal components for improved interpretability. The residual-based detector identifies abnormal fluctuations by analyzing prediction errors, while the risk head predicts potential financial distress using learned latent embeddings. Experiments conducted on the S&P 500, NASDAQ Composite, and Brent Crude Oil datasets (2000-2024) demonstrate the superiority of the proposed model over benchmark methods, achieving a 15.7 percent reduction in RMSE and an 11.5 percent improvement in F1-score for anomaly detection. These results confirm the effectiveness of the model in capturing financial volatility, enabling reliable early-warning systems for market crash prediction and risk management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
