A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series

TL;DR

This paper introduces a FEDformer-based hybrid framework that enhances anomaly detection and risk forecasting in financial time series by modeling both time and frequency domain features, outperforming traditional models.

Contribution

The study presents a novel FEDformer-based hybrid model integrating frequency domain analysis with anomaly detection and risk forecasting for financial data.

Findings

Achieved 15.7% reduction in RMSE compared to benchmarks.

Improved F1-score for anomaly detection by 11.5%.

Effectively captures financial volatility for early warning systems.

Abstract

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.