Various machine learning techniques exist to perform regression on temporal data with concept drift occurring. However, there are numerous nonstationary environments where these techniques may fail to either track or detect the changes. This study develops a genetic programming-based predictive model for temporal data with a numerical target that tracks changes in a dataset due to concept drift. When an environmental change is evident, the proposed algorithm reacts to the change by clustering the data and then inducing nonlinear models that describe generated clusters. Nonlinear models become terminal nodes of genetic programming model trees. Experiments were carried out using seven nonstationary datasets and the obtained results suggest that the proposed model yields high adaptation rates and accuracy to several types of concept drifts. Future work will consider strengthening the adaptation to concept drift and the fast implementation of genetic programming on GPUs to provide fast learning for high-speed temporal data.

存在各种机器学习技术，以在发生概念漂移的情况下对时间数据执行回归。但是，在许多非平稳环境中，这些技术可能无法跟踪或检测到更改。这项研究针对时间数据开发了一种基于遗传编程的预测模型，该模型具有一个数字目标，该数字目标可以跟踪由于概念漂移而导致的数据集中的变化。当明显的环境变化时，所提出的算法通过对数据进行聚类，然后引入描述生成的聚类的非线性模型来对变化做出反应。非线性模型成为遗传编程模型树的终端节点。使用七个非平稳数据集进行了实验，获得的结果表明，所提出的模型对几种类型的概念漂移具有较高的自适应率和准确性。