Anxian Liu

Yuanjian Xu, Anxian Liu, Jianing Hao et al.

Modeling large-scale time series has gained significant attention in recent years. However, its direct application in finance remains challenging due to substantial differences in data characteristics across domains. Specifically, financial systems feature inherent stochasticity and low signal-to-noise ratios, rendering traditional methods and pre-training approaches ineffective. This underscores the urgent need for a foundation model tailored to financial time series. To bridge this gap, we propose \textbf{LENS}, a pre-trained model for this domain. \textbf{LENS} effectively captures the complexity of financial stochastic systems through a carefully crafted model architecture and mitigates noise during pre-training by using an invertible embedding module. We provide a rigorous theoretical explanation of the model's effectiveness and validate its performance through extensive experiments. Pre-trained on a dataset comprising 100 billion financial observations, \textbf{LENS} achieves exceptional results across a wide range of critical downstream tasks. Moreover, our work offers practical insights into developing pre-trained time series models in high-noise environments, paving the way for further advancements in this pivotal research domain.

Anxian Liu, Junying Ma, Guang Zhang

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.