Time Series Shapelets
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.
Summary¶
Introduces shapelets: short discriminative subsequences that can classify time series and explain the decision through localized patterns. The method addresses both efficiency and interpretability limitations of nearest-neighbor time-series classification.