articleJun 28, 2009Closed access
Time series shapelets
University of California, Riverside
Indexed incrossref
Abstract
Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data.
Citation impact
956
total citations
- FWCI
- 19.64
- Percentile
- 100%
- References
- 14
Citations per year
Authors
2Topics & keywords
Topics
Keywords
- Computer science
- k-nearest neighbors algorithm
- Simplicity
- Series (stratigraphy)
- Simple (philosophy)
- Time series
- Nearest neighbor search
- Algorithm
UN Sustainable Development Goals
- Decent work and economic growth
No related works found for this paper.