arXiv:2502.18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies. Not surprisingly, this area has attracted considerable attention from researchers, who have proposed a wide range of methods based on various backbones. However, the evaluation of the area often exhibits three systemic limitations: 1. Failure to account for the full spectrum of stock movement patterns observed in dynamic financial markets. (Diver
The proliferation of AI in finance necessitates rigorous, standardized benchmarking to validate models and ensure robust performance in dynamic markets.
A comprehensive benchmark for financial time series forecasting is crucial for advancing AI-driven investment strategies and improving the reliability of models in real-world scenarios.
The introduction of FinTSB provides a more standardized and holistic evaluation framework, potentially leading to more reliable and generalizable AI applications in finance.
- · AI/ML researchers in finance
- · Quantitative hedge funds
- · Financial data providers
- · Investors utilizing AI-driven tools
- · Proprietary, non-standardized financial AI models
- · Firms relying on outdated forecasting methodologies
Increased transparency and comparability of AI models in financial forecasting.
Faster development and adoption of robust, explainable AI solutions across financial institutions.
Potential for new financial products and services built upon more reliable predictive intelligence, impacting market efficiency and investment returns.
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Read at arXiv cs.LG