Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
arXiv:2603.16985v2 Announce Type: replace Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibility. However, many Transformer variants implicitly assume stationarity and stable temporal dynamics -- assumptions routinely violated in financial markets characterized by regime shifts and non-stationarity. Empirically, state-of-the-art time-series Transformers often underperform even vanilla Transformers on financial tasks, while simpler architectures with distinct inductive biases, such as CNNs
The proliferation of Transformer models in AI has led to their application across diverse fields, necessitating adaptations for specific domain challenges like financial non-stationarity.
Improving AI's ability to forecast financial time series more accurately directly impacts investment strategies, risk management, and the stability of financial markets.
The ability to integrate inductive biases into Transformers for financial time series could lead to more robust and reliable AI-driven financial prediction tools, moving beyond generic architectural assumptions.
- · Financial institutions and hedge funds
- · AI researchers in finance
- · Quantitative traders
- · AI-driven fintech platforms
- · Generic Transformer models without adaptation
- · Traditional statistical forecasting methods
- · Investors relying solely on simple market heuristics
Financial forecasting models become more accurate and resilient to market regime shifts.
Increased reliance on AI in financial decision-making, potentially leading to new forms of algorithmic trading and market dynamics.
Enhanced AI capabilities could reduce market inefficiencies over time, impacting the profitability of certain trading strategies and necessitating more sophisticated approaches.
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Read at arXiv cs.LG