Time series: theory and methods
Time series: theory and methods
Efficient retrieval for browsing large image databases
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Financial time series show the non-linear and fractal characters in the process of time-space kinetics evolution. Traditional dimension reduction methods for similarity query introduce the smoothness to data series in some degree. In the case of unknowing the fractal dimension of financial non- stationary time series, the process of querying the similarity of curve figure will be affected to a certain degree. In this paper, an evaluation formula of varying-time Hurst index is established and the algorithm of varying-time index is presented, and a new determinant standard of series similarity is also introduced. The similarity of curve basic figure is queried and measured at some resolution ratio level. In the meantime, the fractal dimension in local similarity is matched. The effectiveness of the method is validated by means of the simulation examples.