构建金融时间序列评估套件，测试前沿模型真实表现
FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models
- 构建三个金融领域数据集，覆盖真实市场场景
- 引入新指标msIC/msIR，量化模型捕捉时序相关性能力
- 针对金融任务设计评测，适合关注模型落地的从业者
尽管近年来时间序列预测备受关注，诸多研究提出了应对挑战的解决方案以提升预测性能，但将这些模型有效应用于金融资产定价仍面临难题。为此，我们开展以下工作：1）从金融领域构建三个数据集；2）选取十余种近年提出的时间序列预测模型，并在金融时间序列上验证其表现；3）设计新指标msIC和msIR，结合MSE与MAE，展示模型对时序相关性的捕捉能力；4）为这三个数据集设计金融特定任务，评估模型在重要金融问题中的实际表现与应用潜力。我们期望所提出的评估套件FinTSBridge能为先进预测模型在金融领域的有效性与鲁棒性提供关键洞见。
原文摘要 · Abstract (English)
Despite the growing attention to time series forecasting in recent years, many studies have proposed various solutions to address the challenges encountered in time series prediction, aiming to improve forecasting performance. However, effectively applying these time series forecasting models to the field of financial asset pricing remains a challenging issue. There is still a need for a bridge to connect cutting-edge time series forecasting models with financial asset pricing. To bridge this gap, we have undertaken the following efforts: 1) We constructed three datasets from the financial domain; 2) We selected over ten time series forecasting models from recent studies and validated their performance in financial time series; 3) We developed new metrics, msIC and msIR, in addition to MSE and MAE, to showcase the time series correlation captured by the models; 4) We designed financial-specific tasks for these three datasets and assessed the practical performance and application potential of these forecasting models in important financial problems. We hope the developed new evaluation suite, FinTSBridge, can provide valuable insights into the effectiveness and robustness of advanced forecasting models in finanical domains.
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