mlsanityData to 7 Oct 2026

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

arXiv 1611.06455 ↗First posted 20 Nov 2016cs.LG cs.NE stat.MLcited by 358

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Abstract (arXiv)

We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive. The global average pooling in our convolutional model enables the exploitation of the Class Activation Map (CAM) to find out the contributing region in the raw data for the specific labels. Our models provides a simple choice for the real world application and a good starting point for the future research. An overall analysis is provided to discuss the generalization capability of our models, learned features, network structures and the classification semantics.

Key facts. Cited by 358 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Nov 2016.

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Citing papers per year: 2021: 52; 2022: 59; 2023: 76; 2024: 56; 2025: 57; 2026: 58.

358 papers.

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  1. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  2. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 342
  3. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  4. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 99
  5. 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
  6. 2023Non-autoregressive Conditional Diffusion Models for Time Series Predictioncited by 77
  7. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 71
  8. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  9. 2021Gated Transformer Networks for Multivariate Time Series Classificationcited by 58
  10. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  11. 2022Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Seriescited by 54
  12. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
38 more
  1. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 49
  2. 2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 49
  3. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  4. 2022Transformers in Time-series Analysis: A Tutorialcited by 45
  5. 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 44
  6. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  7. 2022Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classificationcited by 39
  8. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  9. 2023Toward a Foundation Model for Time Series Datacited by 28
  10. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 27
  11. 2022Domain Adaptation for Time-Series Classification to Mitigate Covariate Shiftcited by 24
  12. 2021CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learningcited by 23
  13. 2023Explainable AI for Time Series via Virtual Inspection Layerscited by 23
  14. 2023Inherently Interpretable Time Series Classification via Multiple Instance Learningcited by 23
  15. 2022Dynamic Sparse Network for Time Series Classification: Learning What to "see''cited by 22
  16. 2021Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Seriescited by 21
  17. 2025Mantis: Lightweight Foundation Model for Time Series Classificationcited by 21
  18. 2023Label-efficient Time Series Representation Learning: A Reviewcited by 16
  19. 2023TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classificationcited by 16
  20. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  21. 2021Efficient passive membership inference attack in federated learningcited by 14
  22. 2021An Efficient Federated Distillation Learning System for Multi-task Time Series Classificationcited by 14
  23. 2022Distributed Distributionally Robust Optimization with Non-Convex Objectivescited by 14
  24. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  25. 2024Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performancecited by 14
  26. 2021Learnable Dynamic Temporal Pooling for Time Series Classificationcited by 13
  27. 2022Towards Similarity-Aware Time-Series Classificationcited by 13
  28. 2022Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Penscited by 13
  29. 2025Positional Encoding in Transformer-Based Time Series Models: A Surveycited by 13
  30. 2021Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flowscited by 12
  31. 2024Cross-Domain Pre-training with Language Models for Transferable Time Series Representationscited by 12
  32. 2024Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classificationcited by 12
  33. 2021A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Gridscited by 11
  34. 2021Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecastingcited by 11
  35. 2022dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classificationcited by 11
  36. 2021Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noisecited by 10
  37. 2022Backdoor Attacks on Time Series: A Generative Approachcited by 10
  38. 2024M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Mapscited by 10

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