AI4HealthUOL/CausalConceptTS
Repository for the paper 'CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models'.
This tool helps researchers and analysts understand why a time series classification model made a specific prediction. You provide a time series and its classification, and it identifies which segments or 'concepts' within that time series causally influenced the outcome. This is ideal for experts in domains like healthcare, climate science, or neuroscience who need to interpret automated decisions on their sequential data.
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Use this if you need to explain the reasoning behind a time series classification, for example, identifying which part of an ECG trace caused a heart condition diagnosis or which drought patterns led to a specific prediction.
Not ideal if you are looking for a general-purpose time series classification model or if you don't need to understand the 'why' behind the classifications.
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Language
Python
License
MIT
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Last pushed
Jul 15, 2025
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