# TimeX++: Learning Time-Series Explanations with Information Bottleneck

arXiv: https://arxiv.org/abs/2405.09308 · HTML: https://mlsanity.com/paper/2405.09308/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 56 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Formal Limitations on the Measurement of Mutual Information](https://mlsanity.com/paper/1811.04251/) (2018)
- [Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism](https://mlsanity.com/paper/2201.12987/) (2022)
- [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020)
- [Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey](https://mlsanity.com/paper/1911.12116/) (2019)
- [Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/1904.00876/) (2019)
- [TimeSHAP: Explaining Recurrent Models through Sequence Perturbations](https://mlsanity.com/paper/2012.00073/) (2020)
- [Explaining Time Series Predictions with Dynamic Masks](https://mlsanity.com/paper/2106.05303/) (2021)
- [The Information Bottleneck Problem and Its Applications in Machine Learning](https://mlsanity.com/paper/2004.14941/) (2020)
- [Variational Information Bottleneck for Effective Low-Resource Fine-Tuning](https://mlsanity.com/paper/2106.05469/) (2021)
- [OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images](https://mlsanity.com/paper/2111.14341/) (2021)
- [Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency](https://mlsanity.com/paper/2306.02109/) (2023)
- [BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle](https://mlsanity.com/paper/1909.07405/) (2019)
- [Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction](https://mlsanity.com/paper/2006.08849/) (2020)

## Built on by (most cited first, 30 of 33)

- [TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop](https://mlsanity.com/paper/2503.01013/) (2025)
- [F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI](https://mlsanity.com/paper/2410.02970/) (2024)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation](https://mlsanity.com/paper/2506.05035/) (2025)
- [Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback](https://mlsanity.com/paper/2507.15066/) (2025)
- [TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models](https://mlsanity.com/paper/2510.01274/) (2025)
- [FLEXtime: Filterbank learning to explain time series](https://mlsanity.com/paper/2411.05841/) (2024)
- [Visual Instruction Bottleneck Tuning](https://mlsanity.com/paper/2505.13946/) (2025)
- [Adaptive Time Series Reasoning via Segment Selection](https://mlsanity.com/paper/2602.18645/) (2026)
- [Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised Reasoning](https://mlsanity.com/paper/2505.16950/) (2025)
- [ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models](https://mlsanity.com/paper/2510.20084/) (2025)
- [IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck](https://mlsanity.com/paper/2602.22581/) (2026)
- [ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning](https://mlsanity.com/paper/2409.14232/) (2024)
- [Time series saliency maps: explaining models across multiple domains](https://mlsanity.com/paper/2505.13100/) (2025)
- [InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory](https://mlsanity.com/paper/2505.19820/) (2025)
- [DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values](https://mlsanity.com/paper/2507.02342/) (2025)
- [ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting](https://mlsanity.com/paper/2509.23159/) (2025)
- [Information Bottleneck Learning for Faithful Time Series Forecasting Explanations](https://mlsanity.com/paper/2607.28124/) (2026)
- [Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring](https://mlsanity.com/paper/2511.23036/) (2025)
- [A Self-explainable Model of Long Time Series by Extracting Informative Structured Causal Patterns](https://mlsanity.com/paper/2512.01412/) (2025)
- [When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate](https://mlsanity.com/paper/2512.03578/) (2025)
- [TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models](https://mlsanity.com/paper/2601.09776/) (2026)
- [TimeSliver : Symbolic-Linear Decomposition for Explainable Time Series Classification](https://mlsanity.com/paper/2601.21289/) (2026)
- [Interpretability in Deep Time Series Models Demands Semantic Alignment](https://mlsanity.com/paper/2602.02239/) (2026)
- [Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks](https://mlsanity.com/paper/2602.02763/) (2026)
- [Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity](https://mlsanity.com/paper/2607.21573/) (2026)
- [When Does Forecasting Reveal Temporal Structure? A Stability Analysis of Time-Series Structural Selection](https://mlsanity.com/paper/2608.10433/) (2026)
- [ConceptTS: LLM-Guided Concept Bottlenecks for Interpretable Multivariate Time-Series Forecasting](https://mlsanity.com/paper/2608.21277/) (2026)
- [Explaining Time Series Forecasting with Horizon-Resolved Attribution](https://mlsanity.com/paper/2609.12639/) (2026)

## Abstract (arXiv)

Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series signals. In this work, we investigate this problem from an information theoretic perspective and show that most existing measures of explainability may suffer from trivial solutions and distributional shift issues. To address these issues, we introduce a simple yet practical objective function for time series explainable learning. The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues. We further present TimeX++, a novel explanation framework that leverages a parametric network to produce explanation-embedded instances that are both in-distributed and label-preserving. We evaluate TimeX++ on both synthetic and real-world datasets comparing its performance against leading baselines, and validate its practical efficacy through case studies in a real-world environmental application. Quantitative and qualitative evaluations show that TimeX++ outperforms baselines across all datasets, demonstrating a substantial improvement in explanation quality for time series data. The source code is available at urlhttps://github.com/zichuan-liu/TimeXplusplus.