  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.
