FreshPRINCE and DrCIF, two new unsupervised feature-based regressors adapted from time series classification, significantly outperform other methods on an expanded archive of 63 TSER problems and are the only ones to beat rotation forest by a statistically significant margin.
The BOSS is concerned with time series classification in the presence of noise,
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Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
FreshPRINCE and DrCIF, two new unsupervised feature-based regressors adapted from time series classification, significantly outperform other methods on an expanded archive of 63 TSER problems and are the only ones to beat rotation forest by a statistically significant margin.