# Multivariate Time Series Classification with WEASEL+MUSE

arXiv: https://arxiv.org/abs/1711.11343 · HTML: https://mlsanity.com/paper/1711.11343/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 30 of 53)

- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://mlsanity.com/paper/2308.08241/) (2023)
- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification](https://mlsanity.com/paper/2302.09818/) (2023)
- [TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2304.05078/) (2023)
- [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023)
- [HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing](https://mlsanity.com/paper/2202.08055/) (2022)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (2023)
- [ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification](https://mlsanity.com/paper/2405.14608/) (2024)
- [TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning](https://mlsanity.com/paper/2405.03140/) (2024)
- [Deep Efficient Continuous Manifold Learning for Time Series Modeling](https://mlsanity.com/paper/2112.03379/) (2021)
- [MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification](https://mlsanity.com/paper/2503.05582/) (2025)
- [SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series](https://mlsanity.com/paper/2110.00578/) (2021)
- [Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification](https://mlsanity.com/paper/2206.09274/) (2022)
- [Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach](https://mlsanity.com/paper/2303.12533/) (2023)
- [Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields](https://mlsanity.com/paper/2311.18029/) (2023)
- [Unleashing the Power of Shared Label Structures for Human Activity Recognition](https://mlsanity.com/paper/2301.03462/) (2023)
- [ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification](https://mlsanity.com/paper/2302.05021/) (2023)
- [MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs](https://mlsanity.com/paper/2306.03834/) (2023)
- [Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms](https://mlsanity.com/paper/2310.02812/) (2023)
- [Multi-Stage Prototype Learning for Interpretable Time Series Classification](https://mlsanity.com/paper/2106.09636/) (2021)
- [A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification](https://mlsanity.com/paper/2202.05535/) (2022)
- [Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain](https://mlsanity.com/paper/2310.05467/) (2023)
- [Training-Free Time Series Classification via In-Context Reasoning with LLM Agents](https://mlsanity.com/paper/2510.05950/) (2025)
- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data](https://mlsanity.com/paper/2111.08625/) (2021)
- [Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS](https://mlsanity.com/paper/2204.01379/) (2022)
- [LLT: An R package for Linear Law-based Feature Space Transformation](https://mlsanity.com/paper/2304.14211/) (2023)
- [Counterfactual Explainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification](https://mlsanity.com/paper/2511.13237/) (2025)
- [A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting](https://mlsanity.com/paper/2404.13004/) (2024)
- [VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification](https://mlsanity.com/paper/2412.16515/) (2024)

## Abstract (arXiv)

Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. Dealing with this high-dimensional data is challenging for every classifier for at least two aspects: First, an MTS is not only characterized by individual feature values, but also by the interplay of features in different dimensions. Second, this typically adds large amounts of irrelevant data and noise. We present our novel MTS classifier WEASEL+MUSE which addresses both challenges. WEASEL+MUSE builds a multivariate feature vector, first using a sliding-window approach applied to each dimension of the MTS, then extracts discrete features per window and dimension. The feature vector is subsequently fed through feature selection, removing non-discriminative features, and analysed by a machine learning classifier. The novelty of WEASEL+MUSE lies in its specific way of extracting and filtering multivariate features from MTS by encoding context information into each feature. Still the resulting feature set is small, yet very discriminative and useful for MTS classification. Based on a popular benchmark of 20 MTS datasets, we found that WEASEL+MUSE is among the most accurate classifiers, when compared to the state of the art. The outstanding robustness of WEASEL+MUSE is further confirmed based on motion gesture recognition data, where it out-of-the-box achieved similar accuracies as domain-specific methods.