2023
DOI: 10.48550/arxiv.2302.11654
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Information Theory Inspired Pattern Analysis for Time-series Data

Abstract: Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data. Such methods do not generalize well when applied to multivariate, multi-source, state-varying, and noisy time-series data. To address these issues, we propose a highly generalizable method that uses information theory-based features to identify and learn from patterns in multivariate time-series data. To demonstrate the proposed app… Show more

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