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Found 2 papers, 2 papers with code
Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations
In this paper we propose new time series classification algorithms to address these gaps.
Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains
In this work we analyse the state-of-the-art for time series classification, and propose new algorithms that aim to maintain the classifier accuracy and efficiency, but keep interpretability as a key design constraint.
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