# Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition

arXiv: https://arxiv.org/abs/1512.06747 · HTML: https://mlsanity.com/paper/1512.06747/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2015.

## Built on by (most cited first, 22 of 22)

- [TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2304.05078/) (2023)
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- [TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning](https://mlsanity.com/paper/2405.03140/) (2024)
- [Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach](https://mlsanity.com/paper/2303.12533/) (2023)
- [MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs](https://mlsanity.com/paper/2306.03834/) (2023)
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- [A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification](https://mlsanity.com/paper/2202.05535/) (2022)
- [Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability](https://mlsanity.com/paper/2208.11264/) (2022)
- [Improving Accuracy and Explainability of Online Handwriting Recognition](https://mlsanity.com/paper/2209.09102/) (2022)
- [A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFID](https://mlsanity.com/paper/2210.07317/) (2022)
- [FIC-TSC: Learning Time Series Classification with Fisher Information Constraint](https://mlsanity.com/paper/2505.06114/) (2025)
- [A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network](https://mlsanity.com/paper/2101.00702/) (2021)
- [Domain Generalization via Selective Consistency Regularization for Time Series Classification](https://mlsanity.com/paper/2206.07876/) (2022)
- [Approximating DTW with a convolutional neural network on EEG data](https://mlsanity.com/paper/2301.12873/) (2023)
- [Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data](https://mlsanity.com/paper/2411.01070/) (2024)
- [SigTime: Learning and Visually Explaining Time Series Signatures](https://mlsanity.com/paper/2512.12076/) (2025)
- [Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach](https://mlsanity.com/paper/2101.03701/) (2021)
- [Correlation Based Feature Subset Selection for Multivariate Time-Series Data](https://mlsanity.com/paper/2112.03705/) (2021)
- [rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition](https://mlsanity.com/paper/2310.19283/) (2023)
- [Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification](https://mlsanity.com/paper/2403.00565/) (2024)
- [Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations](https://mlsanity.com/paper/2504.17717/) (2025)
- [Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series](https://mlsanity.com/paper/2605.22055/) (2026)

## Abstract (arXiv)

Accurate and computationally efficient means for classifying human activities have been the subject of extensive research efforts. Most current research focuses on extracting complex features to achieve high classification accuracy. We propose a template selection approach based on Dynamic Time Warping, such that complex feature extraction and domain knowledge is avoided. We demonstrate the predictive capability of the algorithm on both simulated and real smartphone data.