Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Optimizing time series discretization for knowledge discovery
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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In a large time series database, similarity searching is a frequent subroutine to find the similar time series of the given one. In the process, the performance of similarity measurement directly effects the usability of the searching results. The proposed methods mostly use the sum of the distances between the values on the time points, e.g. Euclidean Distance, dynamic time warping (DTW) etc. However, in measuring, they do not consider the hierarchy of each point in time series according to importance. This causes that they cannot accurately and efficiently measure similarity of time series. In the paper, we propose a Multi-Hierarchical Representation (MHR) to replace the original one based on the opinion that the points of one time series should be compared with the ones of another with the same importance in measuring. MHR gives the hierarchies of the points, and then the original one can be represented by the Multi-Hierarchical subseries, which consist of points in the same hierarchy. The distance between the representations can be computed as the measuring result. Finally, the synthetic and real data sets were used in the effectiveness experiments comparing ours with other major methods. And the comparison of their efficiencies was also performed on the real data set. All the results showed the superiority of ours in terms of effectiveness and efficiency.