FTW: fast similarity search under the time warping distance
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Faster retrieval with a two-pass dynamic-time-warping lower bound
Pattern Recognition
A BIRCH-Based clustering method for large time series databases
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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In comparison to Euclidean distance, Dynamic Time Warping (DTW) is a much more robust distance measure for time series data. For speeding up DTW computation, a few lower bounding techniques have been proposed in literature to guarantee no false dismissals in time series similarity search. In this work, we apply DTW lower bounding method in time series classification and empirically compare three different typical lower bounding techniques for DTW: LB_Keogh, FTW and LB_Improved in this time series data mining task. Our experimental results show that LB_Keogh and LB_Improved perform well with small warping window widths while FTW is only suitable with large warping window widths or without any constraint on warping windows. Besides, runtime efficiency of LB_Improved is quite poor due to its high complexity in lower bound computation despite of its better pruning power.