Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Exact indexing of dynamic time warping
Knowledge and Information Systems
FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Measuring text similarity with dynamic time warping
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Pattern Recognition by DTW and Series Data Mining in 3D Stratum Modelling and 3D Visualization
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 05
An improved lossless data hiding scheme based on image VQ-index residual value coding
Journal of Systems and Software
iSAX 2.0: Indexing and Mining One Billion Time Series
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
DTW for matching radon features: a pattern recognition and retrieval method
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Expert Systems with Applications: An International Journal
Quaternion Dynamic Time Warping
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
Similarity search is one of the most important tasks in time series data mining, and similarity measure between time series is a basic work. Dynamic time warping (DTW) is often used to compute distance between two time series by warping time axes to match the same shapes. However, its high computation complexity is an obstacle to similarity search based on DTW. To address the issue of similarity search using DTW in time series data mining, an efficient dynamic time warping based on backward strategy and search scope reduction to find the optimal warping path is proposed. At the same time, a small threshold value in the efficient time warping is used to stop similarity measure in advance and to fast expel the dissimilar time series. In this way, a novel similarity search method for time series based on the efficient dynamic time warping without manual intervention is formed. The proposed method searches similar time series with more accuracy and improves its computation speed by the two search scope reductions. The results of experiments on time series datasets demonstrate that in contrast to classical dynamic time warping, the new method can be used to search the similarity in time series databases fast and accurately.