Warped-Linear Models for Time Series Classification
This work addresses time series classification, a domain-specific problem, with incremental improvements in efficiency and quality.
The paper tackles time series classification by proposing warped-linear models as time-warp invariant analogues of linear models, with empirical results showing they better trade solution quality against computation time compared to nearest-neighbor and prototype-based methods.
This article proposes and studies warped-linear models for time series classification. The proposed models are time-warp invariant analogues of linear models. Their construction is in line with time series averaging and extensions of k-means and learning vector quantization to dynamic time warping (DTW) spaces. The main theoretical result is that warped-linear models correspond to polyhedral classifiers in Euclidean spaces. This result simplifies the analysis of time-warp invariant models by reducing to max-linear functions. We exploit this relationship and derive solutions to the label-dependency problem and the problem of learning warped-linear models. Empirical results on time series classification suggest that warped-linear functions better trade solution quality against computation time than nearest-neighbor and prototype-based methods.