# XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

arXiv: https://arxiv.org/abs/2009.04796 · HTML: https://mlsanity.com/paper/2009.04796/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens](https://mlsanity.com/paper/2202.07036/) (2022)
- [A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification](https://mlsanity.com/paper/2202.05535/) (2022)
- [TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series](https://mlsanity.com/paper/2205.13012/) (2022)
- [A Co-design Study for Multi-Stakeholder Job Recommender System Explanations](https://mlsanity.com/paper/2309.05507/) (2023)
- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting](https://mlsanity.com/paper/2305.04876/) (2023)
- [Improving Accuracy and Explainability of Online Handwriting Recognition](https://mlsanity.com/paper/2209.09102/) (2022)
- [An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study](https://mlsanity.com/paper/2504.18578/) (2025)
- [A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography](https://mlsanity.com/paper/2604.22780/) (2026)
- [Time Gated Convolutional Neural Networks for Crop Classification](https://mlsanity.com/paper/2206.09756/) (2022)
- [Interpretable Time Series Clustering Using Local Explanations](https://mlsanity.com/paper/2208.01152/) (2022)
- [Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion](https://mlsanity.com/paper/2302.06683/) (2023)
- [An End-to-End Model for Time Series Classification In the Presence of Missing Values](https://mlsanity.com/paper/2408.05849/) (2024)
- [ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks](https://mlsanity.com/paper/2503.07882/) (2025)
- [Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics](https://mlsanity.com/paper/2510.15950/) (2025)
- [ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts](https://mlsanity.com/paper/2511.02152/) (2025)
- [An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data](https://mlsanity.com/paper/2107.02517/) (2021)
- [Alzheimer's disease detection in PSG signals](https://mlsanity.com/paper/2404.03549/) (2024)
- [Multimodal Physical Fitness Monitoring (PFM) Framework Based on TimeMAE-PFM in Wearable Scenarios](https://mlsanity.com/paper/2404.15294/) (2024)
- [Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data](https://mlsanity.com/paper/2405.07590/) (2024)
- [Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting](https://mlsanity.com/paper/2408.16896/) (2024)
- [PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology](https://mlsanity.com/paper/2504.18329/) (2025)
- [Saliency Map-Guided Knowledge Discovery for Subclass Identification with LLM-Based Symbolic Approximations](https://mlsanity.com/paper/2511.07126/) (2025)
- [Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition](https://mlsanity.com/paper/2605.17685/) (2026)

## Abstract (arXiv)

Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.