# LSTM Fully Convolutional Networks for Time Series Classification

arXiv: https://arxiv.org/abs/1709.05206 · HTML: https://mlsanity.com/paper/1709.05206/

## Key facts

- Cited by 136 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

## Built on by (most cited first, 30 of 136)

- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Gated Transformer Networks for Multivariate Time Series Classification](https://mlsanity.com/paper/2103.14438/) (2021)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning](https://mlsanity.com/paper/2306.13101/) (2023)
- [Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift](https://mlsanity.com/paper/2204.03342/) (2022)
- [AQuA: A Benchmarking Tool for Label Quality Assessment](https://mlsanity.com/paper/2306.09467/) (2023)
- [Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis](https://mlsanity.com/paper/2408.07314/) (2024)
- [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network](https://mlsanity.com/paper/2103.13740/) (2021)
- [Towards Biologically Plausible Convolutional Networks](https://mlsanity.com/paper/2106.13031/) (2021)
- [Towards Similarity-Aware Time-Series Classification](https://mlsanity.com/paper/2201.01413/) (2022)
- [Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens](https://mlsanity.com/paper/2202.07036/) (2022)
- [Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches](https://mlsanity.com/paper/2203.13392/) (2022)
- [Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise](https://mlsanity.com/paper/2105.00349/) (2021)
- [MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification](https://mlsanity.com/paper/2503.05582/) (2025)
- [Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication](https://mlsanity.com/paper/2205.13646/) (2022)
- [Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey](https://mlsanity.com/paper/2310.10060/) (2023)
- [NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting](https://mlsanity.com/paper/2102.05624/) (2021)
- [End-to-End Signal Classification in Signed Cumulative Distribution Transform Space](https://mlsanity.com/paper/2205.00348/) (2022)
- [Unleashing the Power of Shared Label Structures for Human Activity Recognition](https://mlsanity.com/paper/2301.03462/) (2023)
- [ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification](https://mlsanity.com/paper/2302.05021/) (2023)
- [Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network](https://mlsanity.com/paper/2106.03701/) (2021)
- [Predictive Maintenance for General Aviation Using Convolutional Transformers](https://mlsanity.com/paper/2110.03757/) (2021)
- [MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression](https://mlsanity.com/paper/2305.11742/) (2023)
- [Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models](https://mlsanity.com/paper/2408.03636/) (2024)
- [LiteLSTM Architecture for Deep Recurrent Neural Networks](https://mlsanity.com/paper/2201.11624/) (2022)
- [Rethinking Attention Mechanism in Time Series Classification](https://mlsanity.com/paper/2207.07564/) (2022)
- [ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques](https://mlsanity.com/paper/2209.03798/) (2022)

## Abstract (arXiv)

Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our proposed models significantly enhance the performance of fully convolutional networks with a nominal increase in model size and require minimal preprocessing of the dataset. The proposed Long Short Term Memory Fully Convolutional Network (LSTM-FCN) achieves state-of-the-art performance compared to others. We also explore the usage of attention mechanism to improve time series classification with the Attention Long Short Term Memory Fully Convolutional Network (ALSTM-FCN). Utilization of the attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose fine-tuning as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared to other techniques.