{"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/a-memory-network-based-solution-for","title":"A Memory-Network Based Solution for Multivariate Time-Series Forecasting","arxiv_id":"1809.02105","date":"2018-09-06","proceeding":null,"authors":["Yen-Yu Chang","Fan-Yun Sun","Yueh-Hua Wu","Shou-De Lin"],"abstract":"Multivariate time series forecasting is extensively studied throughout the\nyears with ubiquitous applications in areas such as finance, traffic,\nenvironment, etc. Still, concerns have been raised on traditional methods for\nincapable of modeling complex patterns or dependencies lying in real word data.\nTo address such concerns, various deep learning models, mainly Recurrent Neural\nNetwork (RNN) based methods, are proposed. Nevertheless, capturing extremely\nlong-term patterns while effectively incorporating information from other\nvariables remains a challenge for time-series forecasting. Furthermore,\nlack-of-explainability remains one serious drawback for deep neural network\nmodels. Inspired by Memory Network proposed for solving the question-answering\ntask, we propose a deep learning based model named Memory Time-series network\n(MTNet) for time series forecasting. MTNet consists of a large memory\ncomponent, three separate encoders, and an autoregressive component to train\njointly. Additionally, the attention mechanism designed enable MTNet to be\nhighly interpretable. We can easily tell which part of the historic data is\nreferenced the most.","url_abs":"http://arxiv.org/abs/1809.02105v1","url_pdf":"http://arxiv.org/pdf/1809.02105v1.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":"a-memory-network-based-solution-for","repo_url":"https://github.com/fanyun-sun/DARNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"a-memory-network-based-solution-for","repo_url":"https://github.com/Maple728/MTNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02105","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}