CDX-NET: Cross-Domain Multi-Feature Fusion Modeling Via Deep Neural Networks for Multivariate Time Series Forecasting in AIOps

Abstract

In the application of Artificial Intelligence for IT Operations (AIOps), monitoring data are usually modeled as MTS (Multivariate Time Series). The prediction of MTS has been widely studied and various models, including statistic algorithms and deep learning networks, have been proposed, which attempt to capture the multi-dimensional and non-linear features. To this end, this paper focuses on one important type of time series: aperiodic MTS. Our solution introduces a deep neural network named CDX-Net to describe and analyze aperiodic MTS from both temporal and spectral domains. We also propose the integration of the convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism into the predictive model. The introduction of these modules can effectively improve the feature extraction and feature fusion procedures. We conduct performance evaluation on a real-world dataset from an AIOps application and the correlation between the predicted result and ground-truth is found to be significant. The proposed model is compared with several state-of-the-art baseline methods. Empirical results show that our model achieves better performance in most evaluation metrics while others can perform better under some particular settings.