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FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models

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arxiv 2503.06928 v2 pith:QEANSD4I submitted 2025-03-10 cs.LG q-fin.TR

classification cs.LGq-fin.TR
keywords seriestimeforecastingmodelsfinancialperformanceadvancedasset
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. CTBench: Cryptocurrency Time Series Generation Benchmark

    q-fin.ST 2025-08 conditional novelty 6.0 of 10

    CTBench is the first crypto-focused time series generation benchmark, combining forecasting and statistical arbitrage tasks to rank eight generative models.

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