Forecasting the trend of stock prices is an enduring topic at the intersection of finance and computer science. Periodical updates to forecasters have proven effective in handling concept drifts arising from non-stationary markets. However, the existing methods neglect either emerging patterns in recent data or recurring patterns in historical data, both of which are empirically advantageous for future forecasting. To address this issue, we propose meta-learning with dynamic adaptation (MetaDA) for the incremental learning of stock trends, which periodically performs dynamic model adaptation utilizing the emerging and recurring patterns simultaneously. We initially organize the stock trend forecasting into meta-learning tasks and train a forecasting model following meta-learning protocols. During model adaptation, MetaDA efficiently adapts the forecasting model with the latest data and a selected portion of historical data, which is dynamically identified by a task inference module. The task inference module first extracts task-level embeddings from the historical tasks, and then identifies the informative data with a task inference network. MetaDA has been evaluated on real-world stock datasets, achieving state-of-the-art performance with satisfactory efficiency.

翻译：预测股票价格趋势一直是金融与计算机科学交叉领域的一个持久课题。周期性更新预测器已被证明能有效处理非平稳市场引发的概念漂移。然而，现有方法要么忽视近期数据中的新兴模式，要么忽视历史数据中的重复模式，而这两者都对未来预测具有实证优势。为解决此问题，我们提出基于动态适应的元学习（MetaDA）方法，用于股票趋势的增量学习，该方法同步利用新兴与重复模式进行周期性动态模型适应。我们首先将股票趋势预测组织为元学习任务，并按照元学习协议训练预测模型。在模型适应阶段，MetaDA通过任务推理模块动态识别历史数据中的信息子集，结合最新数据与选定历史数据高效调整预测模型。该任务推理模块首先从历史任务中提取任务级嵌入，再通过任务推理网络识别信息性数据。MetaDA已在真实股票数据集上评估，以令人满意的效率实现了当前最优性能。