FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Yanlong Xu, Jian Gao, Tiantian Zhang, Hongkang Huang, Shao-Lun Sun, Danny Dongning Zhang, Xiao-Ping |
| author_facet | Wang, Yanlong Xu, Jian Gao, Tiantian Zhang, Hongkang Huang, Shao-Lun Sun, Danny Dongning Zhang, Xiao-Ping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models Wang, Yanlong Xu, Jian Gao, Tiantian Zhang, Hongkang Huang, Shao-Lun Sun, Danny Dongning Zhang, Xiao-Ping Machine Learning Trading and Market Microstructure 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. |
| title | FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models |
| topic | Machine Learning Trading and Market Microstructure |
| url | https://arxiv.org/abs/2503.06928 |