Segmentation of Multivariate Industrial Time Series Data Based on Dynamic Latent Variable Predictability

Time series segmentation is an important vehicle of data mining and extensively applied in the areas of machine learning and anomaly detection. In real world tasks, dynamics widely exist in time series but have been little concerned. This paper proposes an algorithm which can partition a multivariate time series into subsequences at points where the dynamics change. In process industries, the change of dynamics often relates to operation regime change, working condition shifting or faults. Therefore, to segment time series according to change of dynamics can be useful in data preprocessing and getting deep insight of the process in various industrial process monitoring tasks. The proposed algorithm recursively merges neighborhood subsequences through a heuristic bottom-up procedure. The cost of merging is defined as the mutual predictability of the subsequence models which are constructed using the dynamic-inner principal component analysis algorithm. Then, the goal becomes finding the segmentation scheme which minimizes the averaged dynamic prediction errors of each subsequence model. The proposed algorithm is evaluated on both simulated data and the time series data collected from an industrial processing plant. The results show that it outperforms the static principal component analysis based methods.