The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
ADC '01 Proceedings of the 12th Australasian database conference
General match: a subsequence matching method in time-series databases based on generalized windows
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Querying Time Series Data Based on Similarity
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
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases
Data Mining and Knowledge Discovery
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Fast Normalization-Transformed Subsequence Matching in Time-Series Databases
IEICE - Transactions on Information and Systems
Distortion-free predictive streaming time-series matching
Information Sciences: an International Journal
An MBR-safe transform for high-dimensional MBRs in similar sequence matching
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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Moving average transform is known to reduce the effect of noise and has been used in many areas such as econometrics. Previous subsequence matching methods with moving average transform, however, would incur index overhead both in storage space and in update maintenance since the methods should build multiple indexes for supporting arbitrary orders. To solve this problem, we propose a single index approach for subsequence matching that supports moving average transform of arbitrary order. For a single index approach, we first provide the notion of poly-order moving average transform by generalizing the original definition of moving average transform. We then formally prove correctness of the poly-order transform-based subsequence matching. By using the poly-order transform, we also propose two different subsequence matching methods that support moving average transform of arbitrary order. Experimental results for real stock data show that our methods improve average performance significantly, by 22.4 ~ 33.8 times, over the sequential scan.