Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Supporting fast search in time series for movement patterns in multiple scales
Proceedings of the seventh international conference on Information and knowledge management
A fast projection algorithm for sequence data searching
Data & Knowledge Engineering - Special issue: next generation information technologies and systems
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Relevance feedback retrieval of time series data
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Indexing Scheme for Fast Similarity Search in Large Time Series Databases
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
A segment-wise time warping method for time scaling searching
Information Sciences—Informatics and Computer Science: An International Journal
A geometrical solution to time series searching invariant to shifting and scaling
Knowledge and Information Systems
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A segment-wise time warping method for time scaling searching
Information Sciences: an International Journal
A real time hybrid pattern matching scheme for stock time series
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
A review on time series data mining
Engineering Applications of Artificial Intelligence
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
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The efficiency of searching scaling-invariant and shifting-invariant shapes in a set of massive time series data can be improved if searching is performed on an approximated sequence which involves less data but contains all the significant features. However, commonly used smoothing techniques, such as moving averages and best-fitting polylines, usually miss important peaks and troughs and deform the time series. In addition, these techniques are not robust, as they often requires users to supply a set of smoothing parameters which has direct effect on the resultant approximation pattern. To address these problems, an algorithm to construct a lattice structure as an underlying framework for pattern matching is proposed in this paper. As inputs, the algorithm takes a time series and users' requirements of level of detail. The algorithm then identifies all the important peaks and troughs (known as controlm points) in the time series and classifies the points into appropriate layers of the lattice structure. The control points in each layer of the structure form an approximation pattern an yet preserve the overall shape of the original series with approximation error lies within certain bound. The lower the layer, the more precise the approximation pattern is. Putting in another way, the algorithm takes different levels of data smoothing into account. Also, the lattice structure can be indexed to further improve the performance of pattern matching.