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Found 6 papers, 1 papers with code
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies.
When is Early Classification of Time Series Meaningful?
This problem generalizes classic time series classification to ask if we can classify a time series subsequence with sufficient accuracy and confidence after seeing only some prefix of a target pattern.
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress
Time series anomaly detection has been a perennially important topic in data science, with papers dating back to the 1950s.
FastDTW is approximate and Generally Slower than the Algorithm it Approximates
For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains.
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior
Poultry farms are a major contributor to the human food chain.
Time Series Classification to Improve Poultry Welfare
This task superficially appears to be easy, given the dramatic progress in recent years in classifying human behaviors, and given that human behaviors are presumably more complex.
