# RTFN: A Robust Temporal Feature Network for Time Series Classification

arXiv: https://arxiv.org/abs/2011.11829 · HTML: https://mlsanity.com/paper/2011.11829/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [An Efficient Federated Distillation Learning System for Multi-task Time Series Classification](https://mlsanity.com/paper/2201.00011/) (2021)
- [Rethinking Attention Mechanism in Time Series Classification](https://mlsanity.com/paper/2207.07564/) (2022)
- [Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case](https://mlsanity.com/paper/2205.02706/) (2022)
- [Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing](https://mlsanity.com/paper/2309.11427/) (2023)
- [Multivariate time series classification with dual attention network](https://mlsanity.com/paper/2308.13968/) (2023)
- [Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning](https://mlsanity.com/paper/2309.04420/) (2023)
- [Towards Financially Inclusive Credit Products Through Financial Time Series Clustering](https://mlsanity.com/paper/2402.11066/) (2024)
- [DipMe: Haptic Recognition of Granular Media for Tangible Interactive Applications](https://mlsanity.com/paper/2411.08641/) (2024)
- [Causal and Local Correlations Based Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2411.18008/) (2024)
- [A Framework for Feature Discovery in Intracranial Pressure Monitoring Data Using Neural Network Attention](https://mlsanity.com/paper/2601.07691/) (2026)

## Abstract (arXiv)

Time series data usually contains local and global patterns. Most of the existing feature networks pay more attention to local features rather than the relationships among them. The latter is, however, also important yet more difficult to explore. To obtain sufficient representations by a feature network is still challenging. To this end, we propose a novel robust temporal feature network (RTFN) for feature extraction in time series classification, containing a temporal feature network (TFN) and an LSTM-based attention network (LSTMaN). TFN is a residual structure with multiple convolutional layers. It functions as a local-feature extraction network to mine sufficient local features from data. LSTMaN is composed of two identical layers, where attention and long short-term memory (LSTM) networks are hybridized. This network acts as a relation extraction network to discover the intrinsic relationships among the extracted features at different positions in sequential data. In experiments, we embed RTFN into a supervised structure as a feature extractor and into an unsupervised structure as an encoder, respectively. The results show that the RTFN-based structures achieve excellent supervised and unsupervised performance on a large number of UCR2018 and UEA2018 datasets.