XAI for time series
See:
Resources
- https://github.com/JHoelli/Awesome-Time-Series-Explainability
- Explainable AI for Time Series - Literature Review | Youtube
Code
- #CODE TSInterpret: An Open-Source Library for the interpretability of time series classifiers
- #CODE Time_interpret: Unified Model Interpretability Library for Time Series
References
- #PAPER #REVIEW An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks (2020)
- #PAPER What went wrong and when? Instance-wise Feature Importance for Time-series Models (2020)
- #PAPER #REVIEW Toward Explainable AI for Regression Models (Letzgus 2021)
- #PAPER #REVIEW Evaluation of interpretability methods for multivariate time series forecasting (2021)
- ozanozyegen/evaluation-local-explanation-time-series: Official repo of the paper "Evaluation of interpretability methods for multivariate time series forecasting"
- Regresion models (forecasting)
- Post-hoc local explanation methods
- Evaluation of local explanations remain largely unexplored for time series forecasting models
- #PAPER #REVIEW Towards a Rigorous Evaluation of Explainability for Multivariate Time Series (2021)
- #PAPER LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts (2021)
- #PAPER Explaining Time Series Predictions with Dynamic Masks (2021)
- JonathanCrabbe/Dynamask: This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series. For more details on the theoretical side, please read our ICML 2021 paper: 'Explaining Time Series Predictions with Dynamic Masks'.
- regression and classification
- saliency method for multivariate time series
- #PAPER FI-SHAP: Explanation of Time Series Forecasting and Improvement of Feature Engineering Based on Boosting Algorithm (2022)
- #PAPER TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models (2022)
- #PAPER Time Interpret: a Unified Model Interpretability Library for Time Series (2023)
- #PAPER Temporal Dependencies in Feature Importance for Time Series Predictions (2023)
- #PAPER CBR-fox: A Case-Based Explanation Method for Time Series Forecasting Models (2023)
- #PAPER TsSHAP: Robust model agnostic feature-based explainability for univariate time series forecasting (2023)
- XAI for time series forecasting
- #PAPER ShapTime: A General XAI Approach for Explainable Time Series Forecasting (2024)
- Zhangyuyi-0825/ShapTime
- parece no tener interpretabilidad local