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awesome-time-series-explainability
A list of (post-hoc) XAI for time series
https://github.com/jhoelli/awesome-time-series-explainability
Last synced: about 12 hours ago
JSON representation
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Forecasting
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Frameworks (Multiple Explanation Types)
- **TsSHAP: Robust model agnostic feature-based explainability for univariate time series forecasting**
- **TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models** - ukon/ts-mule](https://github.com/dbvis-ukon/ts-mule), 
- **ShapTime: A General XAI Approach for Explainable Time Series Forecasting** - 0825/ShapTime](https://github.com/Zhangyuyi-0825/ShapTime), 
- **ShapTime: A General XAI Approach for Explainable Time Series Forecasting** - 0825/ShapTime](https://github.com/Zhangyuyi-0825/ShapTime), 
- **TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models** - ukon/ts-mule](https://github.com/dbvis-ukon/ts-mule), 
- **ShapTime: A General XAI Approach for Explainable Time Series Forecasting** - 0825/ShapTime](https://github.com/Zhangyuyi-0825/ShapTime), 
- **TsSHAP: Robust model agnostic feature-based explainability for univariate time series forecasting**
- **Counterfactual Explanations for Time Series Forecasting** - explanations-for-forecasting](https://github.com/zhendong3wang/counterfactual-explanations-for-forecasting), 
- **TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models** - ukon/ts-mule](https://github.com/dbvis-ukon/ts-mule), 
- **Explaining time series predictions with dynamic masks**
- **Series saliency: Temporal interpretation for multivariate time series forecasting**
- **ShapTime: A General XAI Approach for Explainable Time Series Forecasting** - 0825/ShapTime](https://github.com/Zhangyuyi-0825/ShapTime), 
- **TsSHAP: Robust model agnostic feature-based explainability for univariate time series forecasting**
- **Counterfactual Explanations for Time Series Forecasting** - explanations-for-forecasting](https://github.com/zhendong3wang/counterfactual-explanations-for-forecasting), 
- **TEMPORAL DEPENDENCIES IN FEATURE IMPORTANCE FOR TIME SERIES PREDICTION** - labs/WinIT](https://github.com/layer6ai-labs/WinIT), 
- **TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models** - ukon/ts-mule](https://github.com/dbvis-ukon/ts-mule), 
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Surveys
- **Explainable artificial intelligence (XAI) in finance: a systematic literature review**
- **Explainable artificial intelligence (XAI) in finance: a systematic literature review**
- **Explainable artificial intelligence (XAI) in finance: a systematic literature review**
- **A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting**
- **Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective**
- **Explainable AI for time series classification: a review, taxonomy and research directions**
- **Explainable artificial intelligence (xai) on timeseries data: A survey** - Rodríguez, N.*
- **XAI Methods for Neural Time Series Classification: A Brief Review**
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Classification
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Feature Attribution
- **Class-Specific Explainability for Deep Time Series Classifiers**
- **Tsxplain: Demystification of dnn decisions for time-series using natural language and statistical features**
- **Tsxplain: Demystification of dnn decisions for time-series using natural language and statistical features**
- **Translating Image XAI to Multivariate Time Series** - xai-mts](https://github.com/ltronchin/translating-xai-mts), 
- **Explaining time series classifiers through meaningful perturbation and optimisation**
- **Explainable AI for Time Series via Virtual Inspection Layers**
- **LIMESegment: Meaningful, Realistic Time Series Explanations**
- **Class-Specific Explainability for Deep Time Series Classifiers**
- **Translating Image XAI to Multivariate Time Series** - xai-mts](https://github.com/ltronchin/translating-xai-mts), 
- **Explaining time series classifiers through meaningful perturbation and optimisation**
- **Explainable AI for Time Series via Virtual Inspection Layers**
- **LIMESegment: Meaningful, Realistic Time Series Explanations**
- **Class-Specific Explainability for Deep Time Series Classifiers**
- **TSInsight: A local-global attribution framework for interpretability in time series data**
- **Benchmarking Deep Learning Interpretability in Time Series Predictions** - Interpretability-Benchmark](https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark), 
- **timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers**
- **Tsxplain: Demystification of dnn decisions for time-series using natural language and statistical features**
- **What went wrong and when? Instance-wise feature importance for time-series black-box models**
- **Agnostic Local Explanation for Time Series Classification**
- **timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers**
- **Tsxplain: Demystification of dnn decisions for time-series using natural language and statistical features**
- **TSInsight: A local-global attribution framework for interpretability in time series data**
- **Benchmarking Deep Learning Interpretability in Time Series Predictions** - Interpretability-Benchmark](https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark), 
- **What went wrong and when? Instance-wise feature importance for time-series black-box models**
- **Agnostic Local Explanation for Time Series Classification**
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Counterfactuals
- **Attention-Based Counterfactual Explanation for Multivariate Time Series** - based-cf](https://sites.google.com/view/attention-based-cf)
- **Motif-Guided Time Series Counterfactual Explanations** - guided-counterfactual-explanation](https://github.com/Luckilyeee/Motif-guided-counterfactual-explanation), 
- **Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems** - SpaCE](https://github.com/MarioRefoyo/Sub-SpaCE), 
- - time-series-counterfactuals](https://github.com/zhendong3wang/learning-time-series-counterfactuals), 
- **Attention-Based Counterfactual Explanation for Multivariate Time Series** - based-cf](https://sites.google.com/view/attention-based-cf)
- **Motif-Guided Time Series Counterfactual Explanations** - guided-counterfactual-explanation](https://github.com/Luckilyeee/Motif-guided-counterfactual-explanation), 
- **Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems** - SpaCE](https://github.com/MarioRefoyo/Sub-SpaCE), 
- - time-series-counterfactuals](https://github.com/zhendong3wang/learning-time-series-counterfactuals), 
- **Instance-based Counterfactual Explanations for Time Series Classification** - delaney/Instance-Based_CFE_TSC](https://github.com/e-delaney/Instance-Based_CFE_TSC), 
- **Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems** - SpaCE](https://github.com/MarioRefoyo/Sub-SpaCE), 
- **CELS: Counterfactual Explanations for Time Series Data via Learned Saliency Maps**
- - time-series-counterfactuals](https://github.com/zhendong3wang/learning-time-series-counterfactuals), 
- **Attention-Based Counterfactual Explanation for Multivariate Time Series** - based-cf](https://sites.google.com/view/attention-based-cf)
- **Shapelet-Based Counterfactual Explanations for Multivariate Time Series**
- **TSEvo: Evolutionary counterfactual explanations for time series classification**
- **Counterfactual explanations for multivariate time series**
- **Motif-Guided Time Series Counterfactual Explanations** - guided-counterfactual-explanation](https://github.com/Luckilyeee/Motif-guided-counterfactual-explanation), 
- **Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems** - SpaCE](https://github.com/MarioRefoyo/Sub-SpaCE), 
- **CELS: Counterfactual Explanations for Time Series Data via Learned Saliency Maps**
- - time-series-counterfactuals](https://github.com/zhendong3wang/learning-time-series-counterfactuals), 
- **Attention-Based Counterfactual Explanation for Multivariate Time Series** - based-cf](https://sites.google.com/view/attention-based-cf)
- **Shapelet-Based Counterfactual Explanations for Multivariate Time Series**
- **TSEvo: Evolutionary counterfactual explanations for time series classification**
- **Counterfactual explanations for multivariate time series**
- **Motif-Guided Time Series Counterfactual Explanations** - guided-counterfactual-explanation](https://github.com/Luckilyeee/Motif-guided-counterfactual-explanation), 
- **Instance-based Counterfactual Explanations for Time Series Classification** - delaney/Instance-Based_CFE_TSC](https://github.com/e-delaney/Instance-Based_CFE_TSC), 
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Frameworks (Multiple Explanation Types)
- **ExTea: An Evolutionary Algorithm-Based Approach for Enhancing Explainability in Time-Series Models**
- **ExTea: An Evolutionary Algorithm-Based Approach for Enhancing Explainability in Time-Series Models**
- **TimeSHAP: Explaining Recurrent Models through Sequence Perturbations**
- **ExTea: An Evolutionary Algorithm-Based Approach for Enhancing Explainability in Time-Series Models**
- **TimeSHAP: Explaining Recurrent Models through Sequence Perturbations**
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Benchmarking and Evaluation
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Frameworks (Multiple Explanation Types)
- **A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI**
- **XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users**
- **Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series**
- **Evaluation of post-hoc interpretability methods in time-series classification**
- **Robust Framework for Explanation Evaluation in Time Series Classification**
- **Evaluating Explanation Methods for Multivariate Time Series Classification** - Explanation-Methods-for-MTSC](https://github.com/mlgig/Evaluating-Explanation-Methods-for-MTSC), 
- **XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification** - Bench](https://github.com/JHoelli/XTSC-Bench), 
- **Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods**
- **Towards a rigorous evaluation of XAI methods on time series** - Assady, M., Oelke, D., & Keim, D. A.*
- **A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI** - xai-for-time-series/time-series-xai-perturbation-analysis](https://github.com/visual-xai-for-time-series/time-series-xai-perturbation-analysis), 
- **Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions**
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Libraries
- **TSInterpret: A Python Package for the Interpretability of Time Series Classification** - forschungszentrum-informatik/TSInterpret](https://github.com/fzi-forschungszentrum-informatik/TSInterpret), 
- **Time Interpret: a Unified Model Interpretability Library for Time Series**
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Ante-Hoc Explanation
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Classification
- **Retain: An interpretable predictive model for healthcare using reverse time attention mechanism**
- **Fast, accurate and explainable time series classification through randomization** - STSF](https://github.com/stevcabello/r-STSF), 
- **XEM: An explainable-by-design ensemble method for multivariate time series classification**
- **Xcm: An explainable convolutional neural network for multivariate time series classification**
- **Explainable Failure Predictions with RNN Classifiers based on Time Series Data**
- **Explaining Deep Classification of Time-Series Data with Learned Prototypes** - Olano, D., Ghosh, J., & Paydarfar, D.* [https://github.com/alangee/ijcai19-ts-prototypes](https://github.com/alangee/ijcai19-ts-prototypes), 
- **Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis**
- **MTEX-CNN: Multivariate Time Series EXplanations for Predictions with Convolutional Neural Networks**
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Forecasting
- **Temporal fusion transformers for interpretable multi-horizon time series forecasting**
- **Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks** - attention-tcn](https://github.com/lpphd/multivariate-attention-tcn), 
- **Exploring interpretable LSTM neural networks over multi-variable data** - Fantulin* [https://github.com/weilai0980/IMV-LSTM](https://github.com/weilai0980/IMV-LSTM), 
- **A memory-network based solution for multivariate time-series forecasting**
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Classification and Regression / Forcasting
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Frameworks (Multiple Explanation Types)
- **Tsviz: Demystification of deep learning models for time-series analysis** - Core](https://github.com/shoaibahmed/TSViz-Core), 
- **Learning Perturbations to Explain Time Series Predictions**
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