TY - GEN
T1 - Time Series Retrieval Using DTW-Preserving Shapelets
AU - Sperandio, Ricardo Carlini
AU - Malinowski, Simon
AU - Amsaleg, Laurent
AU - Tavenard, Romain
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Dynamic Time Warping (DTW) is a very popular similarity measure used for time series classification, retrieval or clustering. DTW is, however, a costly measure, and its application on numerous and/or very long time series is difficult in practice. This paper proposes a new approach for time series retrieval: time series are embedded into another space where the search procedure is less computationally demanding, while still accurate. This approach is based on transforming time series into high-dimensional vectors using DTW-preserving shapelets. That transform is such that the relative distance between the vectors in the Euclidean transformed space well reflects the corresponding DTW measurements in the original space. We also propose strategies for selecting a subset of shapelets in the transformed space, resulting in a trade-off between the complexity of the transformation and the accuracy of the retrieval. Experimental results using the well known UCR time series demonstrate the importance of this trade-off.
AB - Dynamic Time Warping (DTW) is a very popular similarity measure used for time series classification, retrieval or clustering. DTW is, however, a costly measure, and its application on numerous and/or very long time series is difficult in practice. This paper proposes a new approach for time series retrieval: time series are embedded into another space where the search procedure is less computationally demanding, while still accurate. This approach is based on transforming time series into high-dimensional vectors using DTW-preserving shapelets. That transform is such that the relative distance between the vectors in the Euclidean transformed space well reflects the corresponding DTW measurements in the original space. We also propose strategies for selecting a subset of shapelets in the transformed space, resulting in a trade-off between the complexity of the transformation and the accuracy of the retrieval. Experimental results using the well known UCR time series demonstrate the importance of this trade-off.
UR - https://www.scopus.com/pages/publications/85055098818
U2 - 10.1007/978-3-030-02224-2_20
DO - 10.1007/978-3-030-02224-2_20
M3 - Conference contribution
AN - SCOPUS:85055098818
SN - 9783030022235
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 257
EP - 270
BT - Similarity Search and Applications - 11th International Conference, SISAP 2018, Proceedings
A2 - Marchand-Maillet, Stéphane
A2 - Silva, Yasin N.
A2 - Chávez, Edgar
PB - Springer Verlag
T2 - 11th International Conference on Similarity Search and Applications, SISAP 2018
Y2 - 7 October 2018 through 9 October 2018
ER -