AI RESEARCH
Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
arXiv CS.LG
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ArXi:2603.16985v1 Announce Type: new 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.