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

TL;DR

This paper introduces DDG-DA, a method that predicts future data distribution changes in streaming data to proactively adapt models, improving performance in non-stationary environments with predictable concept drift.

Contribution

The paper presents a novel approach to forecast and generate training data based on predicted distribution trends, enhancing model adaptation to predictable concept drift.

Findings

Significant improvement in forecasting stock prices, electricity load, and solar irradiance.

Effective data generation based on distribution prediction enhances model accuracy.

Demonstrated superiority over existing methods in real-world tasks.

Abstract

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.