{"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/hierarchical-attention-based-recurrent","title":"Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction","arxiv_id":"1806.00685","date":"2018-06-02","proceeding":null,"authors":["Yunzhe Tao","Lin Ma","Weizhong Zhang","Jian Liu","Wei Liu","Qiang Du"],"abstract":"Time series prediction has been studied in a variety of domains. However, it\nis still challenging to predict future series given historical observations and\npast exogenous data. Existing methods either fail to consider the interactions\namong different components of exogenous variables which may affect the\nprediction accuracy, or cannot model the correlations between exogenous data\nand target data. Besides, the inherent temporal dynamics of exogenous data are\nalso related to the target series prediction, and thus should be considered as\nwell. To address these issues, we propose an end-to-end deep learning model,\ni.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which\nincorporates spatio-temporal feature extraction of exogenous variables and\ntemporal dynamics modeling of target variables into a single framework.\nMoreover, by introducing the hierarchical attention mechanism, HRHN can\nadaptively select the relevant exogenous features in different semantic levels.\nWe carry out comprehensive empirical evaluations with various methods over\nseveral datasets, and show that HRHN outperforms the state of the arts in time\nseries prediction, especially in capturing sudden changes and sudden\noscillations of time series.","url_abs":"http://arxiv.org/abs/1806.00685v1","url_pdf":"http://arxiv.org/pdf/1806.00685v1.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":"hierarchical-attention-based-recurrent","repo_url":"https://github.com/KurochkinAlexey/Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hierarchical-attention-based-recurrent","repo_url":"https://github.com/KurochkinAlexey/Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction/blob/master/README.md","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[{"method_slug":"highway-layer","method_name":"Highway Layer"},{"method_slug":"highway-network","method_name":"Highway Network"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}