Exact Indexing for Massive Time Series Databases under Time Warping Distance
Vit Niennattrakul, Pongsakorn Ruengronghirunya, Chotirat Ann, Ratanamahatana

TL;DR
This paper introduces TWIST, an indexed sequential structure that efficiently accelerates DTW-based similarity search in large time series databases by combining sequential and index access methods, reducing query time significantly.
Contribution
The paper proposes TWIST, a novel indexed sequential structure that improves DTW query efficiency by reducing I/O and CPU costs without false dismissals.
Findings
Achieves several orders of magnitude speedup in query processing.
Reduces number of page accesses and storage requirements.
Outperforms existing methods in efficiency and accuracy.
Abstract
Among many existing distance measures for time series data, Dynamic Time Warping (DTW) distance has been recognized as one of the most accurate and suitable distance measures due to its flexibility in sequence alignment. However, DTW distance calculation is computationally intensive. Especially in very large time series databases, sequential scan through the entire database is definitely impractical, even with random access that exploits some index structures since high dimensionality of time series data incurs extremely high I/O cost. More specifically, a sequential structure consumes high CPU but low I/O costs, while an index structure requires low CPU but high I/O costs. In this work, we therefore propose a novel indexed sequential structure called TWIST (Time Warping in Indexed Sequential sTructure) which benefits from both sequential access and index structure. When a query…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Data Management and Algorithms · Anomaly Detection Techniques and Applications
