DELPHYNE: A Pre-Trained Model for General and Financial Time Series
This addresses the need for better pre-trained models in finance, but it is incremental as it builds on existing time-series modeling approaches.
The paper tackled the problem of existing time-series pre-trained models underperforming on financial benchmarks due to lack of financial data and negative transfer effects, and introduced Delphyne, which achieves competitive performance to existing models with few fine-tuning steps and shows superior performance on various financial tasks.
Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data. Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains. However, across financial applications, existing time-series pre-trained models have not shown boosts in performance over simple finance benchmarks in both zero-shot and fine-tuning settings. This phenomenon occurs because of a i) lack of financial data within the pre-training stage, and ii) the negative transfer effect due to inherently different time-series patterns across domains. Furthermore, time-series data is continuous, noisy, and can be collected at varying frequencies and with varying lags across different variables, making this data more challenging to model than languages. To address the above problems, we introduce a Pre-trained MoDEL for FINance TimE-series (Delphyne). Delphyne achieves competitive performance to existing foundation and full-shot models with few fine-tuning steps on publicly available datasets, and also shows superior performances on various financial tasks.