Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting
This work addresses a critical challenge for investment decisions in turbulent financial markets, though it is incremental as it builds on Model-Agnostic Meta-Learning with specific enhancements for financial data.
The paper tackled the problem of zero-shot financial time series forecasting under abrupt market shifts by proposing a novel meta-task construction method using Gaussian Mixture Models and hard task mining, resulting in significant outperformance over existing approaches with stronger generalization in real-world high-volatility and emerging market data.
Financial time series forecasting in zero-shot settings is critical for investment decisions, especially during abrupt market regime shifts or in emerging markets with limited historical data. While Model-Agnostic Meta-Learning (MAML) approaches show promise, existing meta-task construction strategies often yield suboptimal performance for highly turbulent financial series. To address this, we propose a novel task-construction method that leverages learned embeddings for both meta task and also downstream predictions, enabling effective zero-shot meta-learning. Specifically, we use Gaussian Mixture Models (GMMs) to softly cluster embeddings, constructing two complementary meta-task types: intra-cluster tasks and inter-cluster tasks. By assigning embeddings to multiple latent regimes probabilistically, GMMs enable richer, more diverse meta-learning. This dual approach ensures the model can quickly adapt to local patterns while simultaneously capturing invariant cross-series features. Furthermore, we enhance inter-cluster generalization through hard task mining, which identifies robust patterns across divergent market regimes. Our method was validated using real-world financial data from high-volatility periods and multiple international markets (including emerging markets). The results demonstrate significant out-performance over existing approaches and stronger generalization in zero-shot scenarios.