Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new patterns recurring in the future stock market. However, incremental learning for stock trend forecasting still remains under-explored due to the challenge of distribution shifts (a.k.a. concept drifts). With the stock market dynamically evolving, the distribution of future data can slightly or significantly differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts. Our key insight is to automatically learn how to adapt stock data into a locally stationary distribution in favor of profitable updates. Complemented by data adaptation, we can confidently adapt the model parameters under mitigated distribution shifts. We cast each incremental learning task as a meta-learning task and automatically optimize the adapters for desirable data adaptation and parameter initialization. Experiments on real-world stock datasets demonstrate that DoubleAdapt achieves state-of-the-art predictive performance and shows considerable efficiency.

Prediction over tabular data is often a crucial task in many real-life applications. Recent advances in deep learning give rise to various deep models for tabular data prediction. A common and essential step in these models is to vectorize raw input features in tabular data into dense embeddings. Choosing a suitable dimension for each feature is challenging yet necessary to improve model’s performance and reduce memory cost of model parameters. Existing solutions to embedding dimensionality search always choose dimensions from a restricted candidate set. This restriction improves the search efficiency but would produce suboptimal embedding dimensions that hurt model’s predictive performance. In this paper, we develop AutoSrh, a flexible embedding dimensionality search framework that can select varying dimensions for different features through differentiable optimization. The key idea of AutoSrh is to relax the search space to be continuous and optimize the selection of embedding dimensions via gradient descent. After optimization, AutoSrh performs embedding pruning to derive the mixed embedding dimensions and retrains the model to further improve the performance. Extensive experiments on five real-world tabular datasets demonstrate that AutoSrh can achieve better predictive performance than the existing approaches with 1.1$\sim$1.6x lower training time cost and reserve model’s predictive performance while reducing 50$\sim$95% embedding parameters.