{"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/dreamtime-finding-alexnet-for-time-series","title":"InceptionTime: Finding AlexNet for Time Series Classification","arxiv_id":"1909.04939","date":"2019-09-11","proceeding":null,"authors":["Hassan Ismail Fawaz","Benjamin Lucas","Germain Forestier","Charlotte Pelletier","Daniel F. Schmidt","Jonathan Weber","Geoffrey I. Webb","Lhassane Idoumghar","Pierre-Alain Muller","François Petitjean"],"abstract":"This paper brings deep learning at the forefront of research into Time Series Classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE cannot be applied to many real-world datasets because of its high training time complexity in O(N2 * T4) for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 8 days to learn from a small dataset with N = 1500 time series of short length T = 46. Meanwhile deep learning has received enormous attention because of its high accuracy and scalability. Recent approaches to deep learning for TSC have been scalable, but less accurate than HIVE-COTE. We introduce InceptionTime - an ensemble of deep Convolutional Neural Network (CNN) models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime is on par with HIVE-COTE in terms of accuracy while being much more scalable: not only can it learn from 1,500 time series in one hour but it can also learn from 8M time series in 13 hours, a quantity of data that is fully out of reach of HIVE-COTE.","url_abs":"https://arxiv.org/abs/1909.04939v3","url_pdf":"https://arxiv.org/pdf/1909.04939v3.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":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/hfawaz/InceptionTime","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/BochaoJ-Lilly/InceptionTime","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/Labaien96/InceptionTime-State-of-the-art-Time-Series-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/TheMrGhostman/InceptionTime-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/TheMrGhostman/InceptionTime-for-VAE-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/lpw007/Inception_TSC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/okrasolar/pytorch-timeseries","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/teptwomey/deep_learning_architectures_for_fscv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/flaviagiammarino/inception-time-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dreamtime-finding-alexnet-for-time-series","repo_url":"https://github.com/timeseriesAI/tsai/blob/main/tsai/models/InceptionTime.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"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":[{"method_slug":"inceptiontime","method_name":"InceptionTime"}],"datasets_introduced":[],"methods_introduced":[{"slug":"inceptiontime","name":"InceptionTime","full_name":"InceptionTime"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1909.04939","atlas_url":"https://app.syntology.ai/?focus=1909.04939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.04939"}},"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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