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

arXiv: https://arxiv.org/abs/2301.12495 · HTML: https://mlsanity.com/paper/2301.12495/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 178 references that are in the map.
- First posted on arXiv 29 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Applications of shapelet transform to time series classification of earthquake, wind and wave data](https://mlsanity.com/paper/2004.11243/) (2020)
- [Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data](https://mlsanity.com/paper/2005.12742/) (2020)
- [The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT](https://mlsanity.com/paper/2105.03371/) (2021)
- [m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series](https://mlsanity.com/paper/1708.07942/) (2017)
- [Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey](https://mlsanity.com/paper/1801.01704/) (2018)
- [Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data](https://mlsanity.com/paper/2106.04920/) (2021)
- [An Evaluation of Classification Methods for 3D Printing Time-Series Data](https://mlsanity.com/paper/2010.00903/) (2020)

## Built on by (most cited first, 13 of 13)

- [Frequency Disentangled Features in Neural Image Compression](https://mlsanity.com/paper/2308.02620/) (2023)
- [MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification](https://mlsanity.com/paper/2503.05582/) (2025)
- [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-Context Dual Hyper-Prior Neural Image Compression](https://mlsanity.com/paper/2309.10799/) (2023)
- [TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis](https://mlsanity.com/paper/2510.06063/) (2025)
- [QuAnTS: Question Answering on Time Series](https://mlsanity.com/paper/2511.05124/) (2025)
- [Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach](https://mlsanity.com/paper/2311.04148/) (2023)
- [Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms](https://mlsanity.com/paper/2411.17499/) (2024)
- [Multivariate time series classification with dual attention network](https://mlsanity.com/paper/2308.13968/) (2023)
- [Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning](https://mlsanity.com/paper/2407.21503/) (2024)
- [Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems](https://mlsanity.com/paper/2506.21502/) (2025)
- [Machine Learning for Pattern Detection in Printhead Nozzle Logging](https://mlsanity.com/paper/2509.25235/) (2025)
- [FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification](https://mlsanity.com/paper/2608.08207/) (2026)

## 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.