mlsanityData to 7 Oct 2026

Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology

arXiv 2301.12495 ↗First posted 29 Jan 2023cs.LGcited by 13

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Abstract (arXiv)

Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the acquisition of vast amounts of data from diverse sources such as machine tools, robust and affordable sensor systems with advanced information models, and other sources within Smart Manufacturing Systems (SMS). As a result, the amount of data that is available in manufacturing settings has exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and Machine Learning (ML) to be leveraged. Time-series analytics has been successfully applied in a variety of industries, and that success is now being migrated to pattern recognition applications in manufacturing to support higher quality products, zero defect manufacturing, and improved customer satisfaction. However, the diverse landscape of manufacturing presents a challenge for successfully solving problems in industry using time-series pattern recognition. The resulting research gap of understanding and applying the subject matter of time-series pattern recognition in manufacturing is a major limiting factor for adoption in industry. The purpose of this paper is to provide a structured perspective of the current state of time-series pattern recognition in manufacturing with a problem-solving focus. By using an ontology to classify and define concepts, how they are structured, their properties, the relationships between them, and considerations when applying them, this paper aims to provide practical and actionable guidelines for application and recommendations for advancing time-series analytics.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 13 of its 178 references that are in the map. First posted on arXiv 29 Jan 2023.

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13 of 178 references.

References · 13

  1. 2018Deep learning for time series classification: a reviewcited by 575
  2. 2018The UCR Time Series Archivecited by 421
  3. 2018The UEA multivariate time series classification archive, 2018cited by 330
  4. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  5. 2020Benchmarking Multivariate Time Series Classification Algorithmscited by 145
  6. 2014Highly comparative feature-based time-series classificationcited by 39
  7. 2020Applications of shapelet transform to time series classification of earthquake, wind and wave datacited by 8
  8. 2020Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Datacited by 6
  9. 2021The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoTcited by 6
  10. 2017m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Seriescited by 4
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  12. 2021Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Datacited by 4
1 more
  1. 2020An Evaluation of Classification Methods for 3D Printing Time-Series Datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 2; 2025: 5; 2026: 1.

13 papers.

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  1. 2023Frequency Disentangled Features in Neural Image Compressioncited by 14
  2. 2025MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classificationcited by 10
  3. 2023Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithmscited by 7
  4. 2023Multi-Context Dual Hyper-Prior Neural Image Compressioncited by 6
  5. 2025TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysiscited by 5
  6. 2025QuAnTS: Question Answering on Time Seriescited by 5
  7. 2023Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approachcited by 4
  8. 2024Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithmscited by 1
  9. 2023Multivariate time series classification with dual attention networkcited by 0
  10. 2024Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learningcited by 0
  11. 2025Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systemscited by 0
  12. 2025Machine Learning for Pattern Detection in Printhead Nozzle Loggingcited by 0
1 more
  1. 2026FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classificationcited by 0