{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lstm-fully-convolutional-networks-for-time","title":"LSTM Fully Convolutional Networks for Time Series Classification","arxiv_id":"1709.05206","date":"2017-09-08","proceeding":null,"authors":["Fazle Karim","Somshubra Majumdar","Houshang Darabi","Shun Chen"],"abstract":"Fully convolutional neural networks (FCN) have been shown to achieve\nstate-of-the-art performance on the task of classifying time series sequences.\nWe propose the augmentation of fully convolutional networks with long short\nterm memory recurrent neural network (LSTM RNN) sub-modules for time series\nclassification. Our proposed models significantly enhance the performance of\nfully convolutional networks with a nominal increase in model size and require\nminimal preprocessing of the dataset. The proposed Long Short Term Memory Fully\nConvolutional Network (LSTM-FCN) achieves state-of-the-art performance compared\nto others. We also explore the usage of attention mechanism to improve time\nseries classification with the Attention Long Short Term Memory Fully\nConvolutional Network (ALSTM-FCN). Utilization of the attention mechanism\nallows one to visualize the decision process of the LSTM cell. Furthermore, we\npropose fine-tuning as a method to enhance the performance of trained models.\nAn overall analysis of the performance of our model is provided and compared to\nother techniques.","url_abs":"http://arxiv.org/abs/1709.05206v1","url_pdf":"http://arxiv.org/pdf/1709.05206v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/titu1994/LSTM-FCN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/houshd/LSTM-FCN","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/flaviagiammarino/lstm-fcn-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/houshd/MLSTM-FCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/kmutya/Algorithms-for-Drowsy-Driver-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/phuijse/MATIC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/titu1994/MLSTM-FCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/roytalman/LSTM-FCN-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"lstm-fully-convolutional-networks-for-time","repo_url":"https://github.com/timeseriesAI/tsai/tree/main/tsai/models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"lstm","method_name":"LSTM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/outlier-detection-on-ecg5000","task":"Outlier Detection","dataset":"ECG5000","model":"F-t ALSTM-FCN","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"0.9496"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1709.05206","atlas_url":"https://app.syntology.ai/?focus=1709.05206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.05206"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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