{"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/timenet-pre-trained-deep-recurrent-neural","title":"TimeNet: Pre-trained deep recurrent neural network for time series classification","arxiv_id":"1706.08838","date":"2017-06-23","proceeding":null,"authors":["Pankaj Malhotra","Vishnu Tv","Lovekesh Vig","Puneet Agarwal","Gautam Shroff"],"abstract":"Inspired by the tremendous success of deep Convolutional Neural Networks as\ngeneric feature extractors for images, we propose TimeNet: a deep recurrent\nneural network (RNN) trained on diverse time series in an unsupervised manner\nusing sequence to sequence (seq2seq) models to extract features from time\nseries. Rather than relying on data from the problem domain, TimeNet attempts\nto generalize time series representation across domains by ingesting time\nseries from several domains simultaneously. Once trained, TimeNet can be used\nas a generic off-the-shelf feature extractor for time series. The\nrepresentations or embeddings given by a pre-trained TimeNet are found to be\nuseful for time series classification (TSC). For several publicly available\ndatasets from UCR TSC Archive and an industrial telematics sensor data from\nvehicles, we observe that a classifier learned over the TimeNet embeddings\nyields significantly better performance compared to (i) a classifier learned\nover the embeddings given by a domain-specific RNN, as well as (ii) a nearest\nneighbor classifier based on Dynamic Time Warping.","url_abs":"http://arxiv.org/abs/1706.08838v1","url_pdf":"http://arxiv.org/pdf/1706.08838v1.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":"timenet-pre-trained-deep-recurrent-neural","repo_url":"https://github.com/kirarenctaon/timenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"timenet-pre-trained-deep-recurrent-neural","repo_url":"https://github.com/paudan/TimeNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"classification","task_name":"General Classification"},{"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.08838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.08838"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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