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
Developing tools for monitoring the correlations among thousands of financial data streams in an online fashion can be interesting and useful work. We aimed to find highly correlative financial data streams in local p...Developing tools for monitoring the correlations among thousands of financial data streams in an online fashion can be interesting and useful work. We aimed to find highly correlative financial data streams in local patterns. A novel distance metric function slope duration distance (SDD) is proposed, which is compatible with the characteristics of actual financial data streams. Moreover, a model monitoring correlations among local patterns (MCALP) is presented, which dramatically decreases the computational cost using an algorithm quickly online segmenting and pruning (QONSP) with O(1) time cost at each time tick t, and our proposed new grid structure. Experimental results showed that MCALP provides an improvement of several orders of magnitude in performance relative to traditional naive linear scan techniques and maintains high precision. Furthermore, the model is incremental, parallelizable, and has a quick response time.展开更多
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.
基金Project (Nos. 2006AA01Z430 and 2007AA01Z309)supported by the National Hi-Tech Research and Development Program (863) of China
文摘Developing tools for monitoring the correlations among thousands of financial data streams in an online fashion can be interesting and useful work. We aimed to find highly correlative financial data streams in local patterns. A novel distance metric function slope duration distance (SDD) is proposed, which is compatible with the characteristics of actual financial data streams. Moreover, a model monitoring correlations among local patterns (MCALP) is presented, which dramatically decreases the computational cost using an algorithm quickly online segmenting and pruning (QONSP) with O(1) time cost at each time tick t, and our proposed new grid structure. Experimental results showed that MCALP provides an improvement of several orders of magnitude in performance relative to traditional naive linear scan techniques and maintains high precision. Furthermore, the model is incremental, parallelizable, and has a quick response time.
