Proposes Adaptive Financial Transformer with regime-gated attention and a composite loss to predict stock returns while claiming to fix backtesting issues and reduce complexity by 15.2%.
TimeXer: Empowering transformers for time series fore- casting with exogeneous variables,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction
Proposes Adaptive Financial Transformer with regime-gated attention and a composite loss to predict stock returns while claiming to fix backtesting issues and reduce complexity by 15.2%.