  Volume prediction is one of the fundamental objectives in the Fintech area,
which is helpful for many downstream tasks, e.g., algorithmic trading. Previous
methods mostly learn a universal model for different stocks. However, this kind
of practice omits the specific characteristics of individual stocks by applying
the same set of parameters for different stocks. On the other hand, learning
different models for each stock would face data sparsity or cold start problems
for many stocks with small capitalization. To take advantage of the data scale
and the various characteristics of individual stocks, we propose a dual-process
meta-learning method that treats the prediction of each stock as one task under
the meta-learning framework. Our method can model the common pattern behind
different stocks with a meta-learner, while modeling the specific pattern for
each stock across time spans with stock-dependent parameters. Furthermore, we
propose to mine the pattern of each stock in the form of a latent variable
which is then used for learning the parameters for the prediction module. This
makes the prediction procedure aware of the data pattern. Extensive experiments
on volume predictions show that our method can improve the performance of
various baseline models. Further analyses testify the effectiveness of our
proposed meta-learning framework.
