A novel robust prediction algorithm based on REMD-MWNN for AIOps

Abstract

AIOps(Artificial Intelligence Operations) is emerging as one of the most important technology in industrial automation, and accurate time series prediction plays a crucial role in it. However, due to the non-linear and non-stationary characteristics of Ops(operations) data, traditional time series forecasting models can not effectively extract good enough sequence data features and result in poor forecasting accuracy. To fully extract the Ops data information and construct a robust prediction model, this paper proposes a hybrid model based on recursive empirical mode decomposition (REMD) and memory wavelet neural network (MWNN) to improve the forecasting accuracy of Ops data. In REMD-MWNN, we first use REMD to decompose the Ops data into multiple intrinsic modal functions (IMF) at different time scales. Then, to make full use of the historical information of the Ops data and reduce the running time, we designed a new memory recurrent neural network, namely MWNN. Next, use MWNN to predict multiple inherent IMFs respectively to obtain the predicted value of the corresponding subsequence. Finally, the final prediction result is obtained by reconstructing the prediction value of each subsequence. In the comprehensive experiment, we selected the Ops data of an open education platform. The experimental results show that, compared with other algorithms, the model proposed in this paper is highly competitive in predicting future changes and capturing the evolution mode of hidden factors. The experimental data involved in this paper can be downloaded from this website: https://github.com/1600383075/REMD-MWNN/tree/master/data.