# PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations

arXiv: https://arxiv.org/abs/2302.10182 · HTML: https://mlsanity.com/paper/2302.10182/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification](https://mlsanity.com/paper/1805.06546/) (2018)
- [Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning](https://mlsanity.com/paper/1907.13177/) (2019)
- [Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation](https://mlsanity.com/paper/2008.09524/) (2020)
- [Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation](https://mlsanity.com/paper/2207.12964/) (2022)

## Built on by (most cited first, 5 of 5)

- [MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis](https://mlsanity.com/paper/2408.07773/) (2024)
- [PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation](https://mlsanity.com/paper/2506.11170/) (2025)
- [Toward Interpretable Evaluation Measures for Time Series Segmentation](https://mlsanity.com/paper/2510.23261/) (2025)
- [A Network Science Approach to Granular Time Series Segmentation](https://mlsanity.com/paper/2505.17640/) (2025)
- [Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory](https://mlsanity.com/paper/2510.09930/) (2025)

## Abstract (arXiv)

The fourth industrial revolution creates ubiquitous sensor data in production plants. To generate maximum value out of these data, reliable and precise time series-based machine learning methods like temporal neural networks are needed. This paper proposes a novel sequence-to-sequence deep learning architecture for time series segmentation called PrecTime which tries to combine the concepts and advantages of sliding window and dense labeling approaches. The general-purpose architecture is evaluated on a real-world industry dataset containing the End-of-Line testing sensor data of hydraulic pumps. We are able to show that PrecTime outperforms five implemented state-of-the-art baseline networks based on multiple metrics. The achieved segmentation accuracy of around 96% shows that PrecTime can achieve results close to human intelligence in operational state segmentation within a testing cycle.