DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting
To better account for these properties, we introduce Direction- and Price-Aware Adaptation Loss (DPAL), which combines pointwise return fitting with directional and price-calibration terms.
ProofPaper ↗
Key points
- Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available.
- However, adaptation objectives that focus primarily on prediction errors at individual time steps do not fully address the needs of financial time series forecasting.
- We apply DPAL within existing TSF-TTA frameworks without modifying the source forecaster or the adapter architecture.
- Across a diverse collection of financial datasets, DPAL reduces reconstructed-price mean squared error in most evaluated settings.
Sources (1)
- [1]DPAL: A Test-Time Adaptation Loss for Financial Time Series ForecastingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:50 PM
To better account for these properties, we introduce Direction- and Price-Aware Adaptation Loss (DPAL), which combines pointwise return fitting with directional and price-calibration terms.
Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available.
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