Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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TSseek approximates time series as line segments and regex queries as bounding rectangles, then uses a distributed spatial index (TSseek-X) to support efficient exact whole-matching and subsequence-matching queries.
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
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Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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TSseek: Regular Expression-Based Similarity Search for Distributed Time Series Datasets
TSseek approximates time series as line segments and regex queries as bounding rectangles, then uses a distributed spatial index (TSseek-X) to support efficient exact whole-matching and subsequence-matching queries.
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A Catalog of Data Errors
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.