Using dynamic time warping distances as features for improved time series classification
From MaRDI portal
Publication:1741147
DOI10.1007/s10618-015-0418-xzbMath1411.62255OpenAlexW848133049MaRDI QIDQ1741147
Publication date: 3 May 2019
Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10618-015-0418-x
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items (11)
Sequence graph transform (SGT): a feature embedding function for sequence data mining ⋮ A divisive clustering method for functional data with special consideration of outliers ⋮ Echo state network with a global reversible autoencoder for time series classification ⋮ The bootstrap for testing the equality of two multivariate time series with an application to financial markets ⋮ A novel distance measure based on dynamic time warping to improve time series classification ⋮ Early classification of time series using multi-objective optimization techniques ⋮ A review on distance based time series classification ⋮ Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations ⋮ Speeding up similarity search under dynamic time warping by pruning unpromising alignments ⋮ Deep learning for time series classification: a review ⋮ A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size
Uses Software
Cites Work
This page was built for publication: Using dynamic time warping distances as features for improved time series classification
