  In many real-world scenarios, we often deal with streaming data that is
sequentially collected over time. Due to the non-stationary nature of the
environment, the streaming data distribution may change in unpredictable ways,
which is known as concept drift. To handle concept drift, previous methods
first detect when/where the concept drift happens and then adapt models to fit
the distribution of the latest data. However, there are still many cases that
some underlying factors of environment evolution are predictable, making it
possible to model the future concept drift trend of the streaming data, while
such cases are not fully explored in previous work.
  In this paper, we propose a novel method DDG-DA, that can effectively
forecast the evolution of data distribution and improve the performance of
models. Specifically, we first train a predictor to estimate the future data
distribution, then leverage it to generate training samples, and finally train
models on the generated data. We conduct experiments on three real-world tasks
(forecasting on stock price trend, electricity load and solar irradiance) and
obtain significant improvement on multiple widely-used models.
