mlsanityData to 14 Oct 2026

Benchmarking Multivariate Time Series Classification Algorithms

arXiv 2007.13156 ↗First posted 26 Jul 2020cs.LG stat.MLcited by 145

Not on the map. Why?

Abstract (arXiv)

Time Series Classification (TSC) involved building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. The main focus has been on univariate TSC, i.e. the problem where each case has a single series and a class label. In reality, it is more common to encounter multivariate TSC (MTSC) problems where multiple series are associated with a single label. Despite this, much less consideration has been given to MTSC than the univariate case. The UEA archive of 30 MTSC problems released in 2018 has made comparison of algorithms easier. We review recently proposed bespoke MTSC algorithms based on deep learning, shapelets and bag of words approaches. The simplest approach to MTSC is to ensemble univariate classifiers over the multivariate dimensions. We compare the bespoke algorithms to these dimension independent approaches on the 26 of the 30 MTSC archive problems where the data are all of equal length. We demonstrate that the independent ensemble of HIVE-COTE classifiers is the most accurate, but that, unlike with univariate classification, dynamic time warping is still competitive at MTSC.

Key facts. Cited by 145 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 25; 2023: 38; 2024: 29; 2025: 25; 2026: 17.

145 papers.

Sort:

By area:

  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
  3. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 101
  4. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  5. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  6. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 50
  7. 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 45
  8. 2023TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoderscited by 44
  9. 2022MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Seriescited by 28
  10. 2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
  11. 2023FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classificationcited by 21
  12. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Modelscited by 18
38 more
  1. 2023WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classificationcited by 15
  2. 2022HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computingcited by 14
  3. 2022A Review and Evaluation of Elastic Distance Functions for Time Series Clusteringcited by 14
  4. 2023Unsupervised Representation Learning for Time Series: A Reviewcited by 14
  5. 2024ShapeFormer: Shapelet Transformer for Multivariate Time Series Classificationcited by 14
  6. 2023Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontologycited by 13
  7. 2024TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learningcited by 12
  8. 2024Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classificationcited by 12
  9. 2022dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classificationcited by 11
  10. 2021Deep Efficient Continuous Manifold Learning for Time Series Modelingcited by 10
  11. 2021SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Seriescited by 9
  12. 2023Unsupervised Feature Based Algorithms for Time Series Extrinsic Regressioncited by 9
  13. 2024Class-incremental Learning for Time Series: Benchmark and Evaluationcited by 9
  14. 2024Look Into the LITE in Deep Learning for Time Series Classificationcited by 9
  15. 2025Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classificationcited by 9
  16. 2021Elastic Similarity and Distance Measures for Multivariate Time Seriescited by 8
  17. 2022Unifying Pairwise Interactions in Complex Dynamicscited by 8
  18. 2022Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classificationcited by 8
  19. 2022Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Descriptioncited by 8
  20. 2022Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifierscited by 8
  21. 2022Reservoir kernels and Volterra seriescited by 8
  22. 2023Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelscited by 8
  23. 2023Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fieldscited by 8
  24. 2024TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Modelscited by 8
  25. 2022Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasetscited by 7
  26. 2023Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithmscited by 7
  27. 2024Right on Time: Revising Time Series Models by Constraining their Explanationscited by 7
  28. 2024QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Versioncited by 7
  29. 2024Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classificationcited by 7
  30. 2021Multi-Stage Prototype Learning for Interpretable Time Series Classificationcited by 6
  31. 2022Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Seriescited by 6
  32. 2025MONSTER: Monash Scalable Time Series Evaluation Repositorycited by 6
  33. 2021Time Series Analysis and Modeling to Forecast: a Surveycited by 5
  34. 2022Rethinking Attention Mechanism in Time Series Classificationcited by 5
  35. 2023Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time seriescited by 5
  36. 2023POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspectivecited by 5
  37. 2024Efficient High-Resolution Time Series Classification via Attention Kronecker Decompositioncited by 5
  38. 2025MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Seriescited by 5

50 most cited of 145 All 145 (JSON)