# dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification

arXiv: https://arxiv.org/abs/2207.12165 · HTML: https://mlsanity.com/paper/2207.12165/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 74 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Benchmarking Deep Learning Interpretability in Time Series Predictions](https://mlsanity.com/paper/2010.13924/) (2020)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019)
- [Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis](https://mlsanity.com/paper/1806.08946/) (2018)
- [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [Towards a universal neural network encoder for time series](https://mlsanity.com/paper/1805.03908/) (2018)
- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)
- [Evaluating surgical skills from kinematic data using convolutional neural networks](https://mlsanity.com/paper/1806.02750/) (2018)
- [Deep learning for time series classification](https://mlsanity.com/paper/2010.00567/) (2020)
- [Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series](https://mlsanity.com/paper/2008.13447/) (2020)
- [Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search](https://mlsanity.com/paper/2006.11459/) (2020)
- [Ensemble Grammar Induction For Detecting Anomalies in Time Series](https://mlsanity.com/paper/2001.11102/) (2020)
- [The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art](https://mlsanity.com/paper/2006.11454/) (2020)
- [Fast Data Series Indexing for In-Memory Data](https://mlsanity.com/paper/2110.07519/) (2021)
- [ParIS+: Data Series Indexing on Multi-Core Architectures](https://mlsanity.com/paper/2009.00166/) (2020)

## Built on by (most cited first, 11 of 11)

- [Appliance Detection Using Very Low-Frequency Smart Meter Time Series](https://mlsanity.com/paper/2305.10352/) (2023)
- [Robust Explainer Recommendation for Time Series Classification](https://mlsanity.com/paper/2306.05501/) (2023)
- [Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2406.12507/) (2024)
- [ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series](https://mlsanity.com/paper/2401.05381/) (2023)
- [CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System](https://mlsanity.com/paper/2405.14691/) (2024)
- [Evaluating Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2308.15223/) (2023)
- [Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series](https://mlsanity.com/paper/2506.05895/) (2025)
- [MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection](https://mlsanity.com/paper/2510.26643/) (2025)
- [DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series](https://mlsanity.com/paper/2506.05912/) (2025)
- [An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification](https://mlsanity.com/paper/2509.03649/) (2025)
- [ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts](https://mlsanity.com/paper/2511.02152/) (2025)

## Abstract (arXiv)

Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decisions is a real need in many applications. Convolutional neural networks perform well for the data series classification task; though, the explanations provided by this type of algorithm are poor for the specific case of multivariate data series. Addressing this important limitation is a significant challenge. In this paper, we propose a novel method that solves this problem by highlighting both the temporal and dimensional discriminant information. Our contribution is two-fold: we first describe a convolutional architecture that enables the comparison of dimensions; then, we propose a method that returns dCAM, a Dimension-wise Class Activation Map specifically designed for multivariate time series (and CNN-based models). Experiments with several synthetic and real datasets demonstrate that dCAM is not only more accurate than previous approaches, but the only viable solution for discriminant feature discovery and classification explanation in multivariate time series. This paper has appeared in SIGMOD'22.