In this paper, two spectral representations of the multidimensional dyadic stationary series and their correlation functions are given,and the isomorphic space of the value space of the multidimensional dyadic station...In this paper, two spectral representations of the multidimensional dyadic stationary series and their correlation functions are given,and the isomorphic space of the value space of the multidimensional dyadic stationary series is constructed.展开更多
Time series motifs are previously unknown,frequently occurring patterns in time series or approximately repeated subsequences that are very similar to each other.There are two issues in time series motifs discovery,th...Time series motifs are previously unknown,frequently occurring patterns in time series or approximately repeated subsequences that are very similar to each other.There are two issues in time series motifs discovery,the deficiency of the definition of K-motifs given by Lin et al.(2002) and the large computation time for extracting motifs.In this paper,we propose a relatively comprehensive definition of K-motifs to obtain more valuable motifs.Tominimize the computation time as much as possible,we extend the triangular inequality pruning method to avoid unnecessary operations and calculations,and propose an optimized matrix structure to produce the candidate motifs almost immediately.Results of two experiments on three time series datasets show that our motifs discovery algorithm is feasible and efficient.展开更多
文摘In this paper, two spectral representations of the multidimensional dyadic stationary series and their correlation functions are given,and the isomorphic space of the value space of the multidimensional dyadic stationary series is constructed.
基金Project supported by the "Nuclear High Base" National Science and Technology Major Project (No.2010ZX01042-001-003)the National Basic Research Program (973) of China (No. 2007CB310804)the National Natural Science Foundation of China (No.61173061)
文摘Time series motifs are previously unknown,frequently occurring patterns in time series or approximately repeated subsequences that are very similar to each other.There are two issues in time series motifs discovery,the deficiency of the definition of K-motifs given by Lin et al.(2002) and the large computation time for extracting motifs.In this paper,we propose a relatively comprehensive definition of K-motifs to obtain more valuable motifs.Tominimize the computation time as much as possible,we extend the triangular inequality pruning method to avoid unnecessary operations and calculations,and propose an optimized matrix structure to produce the candidate motifs almost immediately.Results of two experiments on three time series datasets show that our motifs discovery algorithm is feasible and efficient.
