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
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Time-series forecasting using GA-tuned radial basis functions
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
ADC '01 Proceedings of the 12th Australasian database conference
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
On Similarity-Based Queries for Time Series Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Similarity search of time-warped subsequences via a suffix tree
Information Systems
A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases
Data Mining and Knowledge Discovery
Information Systems - Databases: Creation, management and utilization
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
DDR: an index method for large time-series datasets
Information Systems
A segment-wise time warping method for time scaling searching
Information Sciences—Informatics and Computer Science: An International Journal
Pattern Discovery of Fuzzy Time Series for Financial Prediction
IEEE Transactions on Knowledge and Data Engineering
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
Noise Control Boundary Image Matching Using Time-Series Moving Average Transform
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Scaling-invariant boundary image matching using time-series matching techniques
Data & Knowledge Engineering
| Hi-index | 0.07 |
Moving average transform is very useful in finding the trend of time-series data by reducing the effect of noise, and has been used in many areas such as econometrics. Previous subsequence matching methods with moving average transform, however, are problematic in that, since they must build multiple indexes in supporting transform of arbitrary order, they incur index overhead both in storage space and in update maintenance. To solve this problem, we propose a single-index approach to subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single-index approach, we can reduce both the storage space and the index maintenance overhead. In explaining the single-index approach, we first introduce the notion of poly-order moving average transform by generalizing the original definition of moving average transform. We then formally prove the correctness of poly-order transform-based subsequence matching. We also propose two subsequence matching methods based on poly-order transform that efficiently support moving average transform of arbitrary order. Experimental results for real stock data show that, compared with the sequential scan, our methods improve average performance significantly, by a factor of 22.6-33.6. Also, compared with cases in which an index is built for every moving average order, our methods reduce storage space and maintenance effort significantly while incurring only marginal performance degradation. Our approach entails the additional advantage of being generalized to support many other transforms in addition to moving average transform. Therefore, we believe that our approach will be widely used in many transform-based subsequence matching methods.