Memetic segmentation based on variable lag aware for multivariate time series

Abstract

The memetic segmentation algorithm has been proposed to obtain precise segmentation results through efficient local refinement. However, this algorithm overlooks the consideration of time lags among variables or spatial locations in multivariate time series data. Although some researchers have adopted dynamic programming to eliminate time lags and obtain segmentation results iteratively, they have failed to achieve simultaneous optimization for both lag awareness and segmentation. To address this problem, we propose a memetic segmentation algorithm based on variable lag-aware for multivariate time series (MSVLAMTS), which enhances the encoding operation of the memetic algorithm to optimize both time-lag awareness and segmentation positions. Furthermore, MSVLAMTS incorporates the self-distribution of multivariate time series and the correlation between variables to construct a maximum likelihood function for fitness evaluation, and further improves the optimization strategy of the memetic algorithm. Experiment results on artificial and real datasets demonstrate that MSVLAMTS exhibits superior performance in both time-lag awareness and segmentation. It effectively resolves the challenge of variable time-lag awareness in multivariate time series data and enhances the accuracy of collaborative segmentation.