DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation


  • Wendi Li University of Wisconsin-Madison Microsoft Research Asia
  • Xiao Yang Microsoft Research Asia
  • Weiqing Liu Microsoft Research Asia
  • Yingce Xia Microsoft Research Asia
  • Jiang Bian Microsoft Research Asia




Data Mining & Knowledge Management (DMKM)


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 the concept drift in the literature. 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 obtained significant improvement on multiple widely-used models.




How to Cite

Li, W., Yang, X., Liu, W., Xia, Y., & Bian, J. (2022). DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4092-4100. https://doi.org/10.1609/aaai.v36i4.20327



AAAI Technical Track on Data Mining and Knowledge Management