# Counterfactual Explanations for Machine Learning on Multivariate Time Series Data

arXiv: https://arxiv.org/abs/2008.10781 · HTML: https://mlsanity.com/paper/2008.10781/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 30 of 49)

- [Explainable AI for Time Series via Virtual Inspection Layers](https://mlsanity.com/paper/2303.06365/) (2023)
- [Towards Explainable Traffic Flow Prediction with Large Language Models](https://mlsanity.com/paper/2404.02937/) (2024)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [Motif-guided Time Series Counterfactual Explanations](https://mlsanity.com/paper/2211.04411/) (2022)
- [Generating Sparse Counterfactual Explanations For Multivariate Time Series](https://mlsanity.com/paper/2206.00931/) (2022)
- [Diverse Counterfactual Explanations for Anomaly Detection in Time Series](https://mlsanity.com/paper/2203.11103/) (2022)
- [Counterfactual Explanations for Time Series Forecasting](https://mlsanity.com/paper/2310.08137/) (2023)
- [M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps](https://mlsanity.com/paper/2411.02649/) (2024)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Shapelet-Based Counterfactual Explanations for Multivariate Time Series](https://mlsanity.com/paper/2208.10462/) (2022)
- [Time Can Invalidate Algorithmic Recourse](https://mlsanity.com/paper/2410.08007/) (2024)
- [Explainable AI needs formalization](https://mlsanity.com/paper/2409.14590/) (2024)
- [MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series](https://mlsanity.com/paper/2503.22389/) (2025)
- [Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series](https://mlsanity.com/paper/2203.07861/) (2022)
- [Explaining a machine learning decision to physicians via counterfactuals](https://mlsanity.com/paper/2306.06325/) (2023)
- [Prototypes as Explanation for Time Series Anomaly Detection](https://mlsanity.com/paper/2307.01601/) (2023)
- [Explaining Time Series Classification Predictions via Causal Attributions](https://mlsanity.com/paper/2405.15871/) (2024)
- [What-If Explanations Over Time: Counterfactuals for Time Series Classification](https://mlsanity.com/paper/2603.27792/) (2026)
- [XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification](https://mlsanity.com/paper/2310.14957/) (2023)
- [PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies](https://mlsanity.com/paper/2401.09489/) (2024)
- [Counterfactual Explanations for Deep Learning-Based Traffic Forecasting](https://mlsanity.com/paper/2405.00456/) (2024)
- [Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification](https://mlsanity.com/paper/2501.04009/) (2024)
- [Counterfactual Explainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification](https://mlsanity.com/paper/2511.13237/) (2025)
- [Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities](https://mlsanity.com/paper/2306.12609/) (2023)
- [TraCE: Trajectory Counterfactual Explanation Scores](https://mlsanity.com/paper/2309.15965/) (2023)
- [Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventions](https://mlsanity.com/paper/2512.14559/) (2025)
- [Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy](https://mlsanity.com/paper/2301.01520/) (2023)
- [Explainable Anomaly Detection: Counterfactual driven What-If Analysis](https://mlsanity.com/paper/2408.11935/) (2024)
- [Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification](https://mlsanity.com/paper/2408.12666/) (2024)
- [TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation](https://mlsanity.com/paper/2511.06529/) (2025)

## Abstract (arXiv)

Applying machine learning (ML) on multivariate time series data has growing popularity in many application domains, including in computer system management. For example, recent high performance computing (HPC) research proposes a variety of ML frameworks that use system telemetry data in the form of multivariate time series so as to detect performance variations, perform intelligent scheduling or node allocation, and improve system security. Common barriers for adoption for these ML frameworks include the lack of user trust and the difficulty of debugging. These barriers need to be overcome to enable the widespread adoption of ML frameworks in production systems. To address this challenge, this paper proposes a novel explainability technique for providing counterfactual explanations for supervised ML frameworks that use multivariate time series data. The proposed method outperforms state-of-the-art explainability methods on several different ML frameworks and data sets in metrics such as faithfulness and robustness. The paper also demonstrates how the proposed method can be used to debug ML frameworks and gain a better understanding of HPC system telemetry data.