Forecasting the trend of stock prices is an enduring topic at the intersection of finance and computer science. Incremental updates to forecasters have proven effective in alleviating the impacts of concept drift arising from non-stationary stock markets. However, there is a need for refinement in the incremental learning of stock trends, as existing methods disregard recurring patterns. To address this issue, we propose meta-learning with dynamic adaptation (MetaDA) for the incremental learning of stock trends, which performs dynamic model adaptation considering both the recurring and emerging patterns. We initially organize the stock trend forecasting into meta-learning tasks and train a forecasting model following meta-learning protocols. During model adaptation, MetaDA 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：预测股票价格趋势是金融与计算机科学交叉领域的持久研究课题。实践表明，对预测模型进行增量更新能有效缓解非平稳股票市场引发的概念漂移影响。然而，现有增量学习方法忽略了股票趋势中存在的循环模式，亟需改进。针对该问题，我们提出基于元学习与动态自适应（MetaDA）的股票趋势增量学习框架，该框架通过动态模型自适应同时兼顾循环模式与新兴模式。我们首先将股票趋势预测任务组织为元学习任务，并遵循元学习协议训练预测模型。在模型自适应阶段，MetaDA利用最新数据及动态选取的历史数据子集（由任务推理模块实时识别）完成模型自适应。该任务推理模块首先从历史任务中提取任务级嵌入表示，再通过任务推理网络识别信息量丰富的历史数据。我们在真实股票数据集上对MetaDA进行了评估，实验结果表明该方法以高效运算实现了最先进的预测性能。