Anomalies in eight popular MTSAD benchmarks are predominantly univariate, with no cross-channel ruptures occurring without accompanying univariate deviations, rendering the benchmarks unsuitable for testing cross-channel modeling.
arXiv preprint arXiv:2408.14763 , year=
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TSQAgent uses three collaborative LLM agents with analytical tools to identify relevant quality dimensions and enable quantitative comparisons for time series data, improving on standard LLM methods and leading to better downstream data selection.
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Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
Anomalies in eight popular MTSAD benchmarks are predominantly univariate, with no cross-channel ruptures occurring without accompanying univariate deviations, rendering the benchmarks unsuitable for testing cross-channel modeling.
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TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
TSQAgent uses three collaborative LLM agents with analytical tools to identify relevant quality dimensions and enable quantitative comparisons for time series data, improving on standard LLM methods and leading to better downstream data selection.