Experiencing the Shotgun Distance for Time Series Analysis
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Summary
The shotgun distance similarity measure is proposed that extracts, scales, and aligns segments from a query to a sample time series, which greatly simplifies the time series analysis task of those time series produced by sensors.
- Type
- article
- Published
- 2014-01-07
- Cited by
- 4
- References
- 27
- OpenAlex
- https://openalex.org/W45744237
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13404415
Keywords
Computer science, Artificial intelligence, Classifier (UML), Cluster analysis, Pattern recognition (psychology)
References
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- Data Preparation for Data Mining
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- SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets
- Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
- Clustering of time series data - a survey
- Querying and mining of time series data: experimental comparison of representations and distance measures
- Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping
- Dynamic programming algorithm optimization for spoken word recognition
- Rotation-invariant similarity in time series using bag-of-patterns representation
- Experiencing SAX: a novel symbolic representation of time series
- On Classifying Insects from their Wing-Beat: New Results
- Time Series Classification under More Realistic Assumptions
- A Complexity-Invariant Distance Measure for Time Series
- Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets
- Sequence the Human Genome
- Efficient Similarity Search In Sequence Databases
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