# What-If Explanations Over Time: Counterfactuals for Time Series Classification

arXiv: https://arxiv.org/abs/2603.27792 · HTML: https://mlsanity.com/paper/2603.27792/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 44 references that are in the map.
- First posted on arXiv 29 Mar 2026.

## Builds on (most cited first, 24 of 24)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Multi-Objective Counterfactual Explanations](https://mlsanity.com/paper/2004.11165/) (2020)
- [CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms](https://mlsanity.com/paper/2108.00783/) (2021)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)](https://mlsanity.com/paper/2005.13997/) (2020)
- [Instance-based Counterfactual Explanations for Time Series Classification](https://mlsanity.com/paper/2009.13211/) (2020)
- [Counterfactual Explanations for Machine Learning on Multivariate Time Series Data](https://mlsanity.com/paper/2008.10781/) (2020)
- [Conditional Generative Models for Counterfactual Explanations](https://mlsanity.com/paper/2101.10123/) (2021)
- [Self-Interpretable Time Series Prediction with Counterfactual Explanations](https://mlsanity.com/paper/2306.06024/) (2023)
- [Motif-guided Time Series Counterfactual Explanations](https://mlsanity.com/paper/2211.04411/) (2022)
- [Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ](https://mlsanity.com/paper/2212.08733/) (2022)
- [Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations](https://mlsanity.com/paper/2012.09301/) (2020)
- [Generating Sparse Counterfactual Explanations For Multivariate Time Series](https://mlsanity.com/paper/2206.00931/) (2022)
- [M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps](https://mlsanity.com/paper/2411.02649/) (2024)
- [Shapelet-Based Counterfactual Explanations for Multivariate Time Series](https://mlsanity.com/paper/2208.10462/) (2022)
- [Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification](https://mlsanity.com/paper/2402.01343/) (2024)
- [Visual Explanations with Attributions and Counterfactuals on Time Series Classification](https://mlsanity.com/paper/2307.08494/) (2023)
- [XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification](https://mlsanity.com/paper/2310.14957/) (2023)
- [Interactive Counterfactual Generation for Univariate Time Series](https://mlsanity.com/paper/2408.10633/) (2024)
- [Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification](https://mlsanity.com/paper/2501.04009/) (2024)
- [Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventions](https://mlsanity.com/paper/2512.14559/) (2025)

## Built on by (most cited first, 4 of 4)

- [ConTex: Reformulating Counterfactual Generation For Time Series Forecasting](https://mlsanity.com/paper/2606.18049/) (2026)
- [ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series](https://mlsanity.com/paper/2607.18748/) (2026)
- [IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution](https://mlsanity.com/paper/2608.04777/) (2026)
- [Counterfactual Transition Graphs: Evaluating Cross-Class Transition Quality](https://mlsanity.com/paper/2608.23164/) (2026)

## Abstract (arXiv)

Counterfactual explanations emerge as a powerful approach in explainable AI, providing what-if scenarios that reveal how minimal changes to an input time series can alter the model's prediction. This work presents a survey of recent algorithms for counterfactual explanations for time series classification. We review state-of-the-art methods, spanning instance-based nearest-neighbor techniques, pattern-driven algorithms, gradient-based optimization, and generative models. For each, we discuss the underlying methodology, the models and classifiers they target, and the datasets on which they are evaluated. We highlight unique challenges in generating counterfactuals for temporal data, such as maintaining temporal coherence, plausibility, and actionable interpretability, which distinguish the temporal from tabular or image domains. We analyze the strengths and limitations of existing approaches and compare their effectiveness along key dimensions (validity, proximity, sparsity, plausibility, etc.). In addition, we implemented an open-source implementation library, Counterfactual Explanations for Time Series (CFTS), as a reference framework that includes many algorithms and evaluation metrics. We discuss this library's contributions in standardizing evaluation and enabling practical adoption of explainable time series techniques. Finally, based on the literature and identified gaps, we propose future research directions, including improved user-centered design, integration of domain knowledge, and counterfactuals for time series forecasting.