{"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/transfer-learning-for-time-series","title":"Transfer learning for time series classification","arxiv_id":"1811.01533","date":"2018-11-05","proceeding":null,"authors":["Hassan Ismail Fawaz","Germain Forestier","Jonathan Weber","Lhassane Idoumghar","Pierre-Alain Muller"],"abstract":"Transfer learning for deep neural networks is the process of first training a\nbase network on a source dataset, and then transferring the learned features\n(the network's weights) to a second network to be trained on a target dataset.\nThis idea has been shown to improve deep neural network's generalization\ncapabilities in many computer vision tasks such as image recognition and object\nlocalization. Apart from these applications, deep Convolutional Neural Networks\n(CNNs) have also recently gained popularity in the Time Series Classification\n(TSC) community. However, unlike for image recognition problems, transfer\nlearning techniques have not yet been investigated thoroughly for the TSC task.\nThis is surprising as the accuracy of deep learning models for TSC could\npotentially be improved if the model is fine-tuned from a pre-trained neural\nnetwork instead of training it from scratch. In this paper, we fill this gap by\ninvestigating how to transfer deep CNNs for the TSC task. To evaluate the\npotential of transfer learning, we performed extensive experiments using the\nUCR archive which is the largest publicly available TSC benchmark containing 85\ndatasets. For each dataset in the archive, we pre-trained a model and then\nfine-tuned it on the other datasets resulting in 7140 different deep neural\nnetworks. These experiments revealed that transfer learning can improve or\ndegrade the model's predictions depending on the dataset used for transfer.\nTherefore, in an effort to predict the best source dataset for a given target\ndataset, we propose a new method relying on Dynamic Time Warping to measure\ninter-datasets similarities. We describe how our method can guide the transfer\nto choose the best source dataset leading to an improvement in accuracy on 71\nout of 85 datasets.","url_abs":"http://arxiv.org/abs/1811.01533v1","url_pdf":"http://arxiv.org/pdf/1811.01533v1.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":"transfer-learning-for-time-series","repo_url":"https://github.com/hfawaz/bigdata18","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object-localization","task_name":"Object Localization"},{"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"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.01533","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}