An active adaptation strategy for streaming time series classification based on elastic similarity measures
No Thumbnail Available
Identifiers
Publication date
2022-05-21
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Citations
Export
Abstract
In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation
strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference
patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.
Description
Type
Citation
Oregi, Izaskun, Aritz Pérez, Javier Del Ser, and Jose A. Lozano. “An Active Adaptation Strategy for Streaming Time Series Classification Based on Elastic Similarity Measures.” Neural Computing and Applications, 2022. https://doi.org/10.1007/s00521-022-07358-3.