Many of the data sets extracted from real-world industrial environments are time series that describe dynamic processes with characteristics that change over time. In this paper, we focus on the fouling process in an industrial furnace, which corresponds to a non-stationary multivariate time series with a seasonal component, non-homogeneous cycles and sporadic human interventions. We aim to forecast the evolution of the temperature inside the furnace over a long span of time of two and a half months. To accomplish this, we model the time series with dynamic Gaussian Bayesian networks (DGBNs) and compare their performance with convolutional recurrent neural networks. Our results show that DGBNs are capable of properly treating seasonal data and can capture the tendency of a time series without being distorted by the effect of interventions or by the varying length of the cycles.

从现实世界的工业环境中提取的许多数据集都是时间序列，描述了具有随时间变化的特征的动态过程。在本文中，我们关注工业炉中的结垢过程，这对应于具有季节成分，非均匀周期和零星的人为干预的非平稳多元时间序列。我们的目的是预测在两个半月的长时间内炉内温度的变化。为此，我们使用动态高斯贝叶斯网络（DGBN）对时间序列进行建模，并将其性能与卷积递归神经网络进行比较。