{"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/autoregressive-convolutional-recurrent-neural","title":"Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction","arxiv_id":"1903.02540","date":"2019-03-06","proceeding":null,"authors":["Matteo Maggiolo","Gerasimos Spanakis"],"abstract":"Time Series forecasting (univariate and multivariate) is a problem of high\ncomplexity due the different patterns that have to be detected in the input,\nranging from high to low frequencies ones. In this paper we propose a new model\nfor timeseries prediction that utilizes convolutional layers for feature\nextraction, a recurrent encoder and a linear autoregressive component. We\nmotivate the model and we test and compare it against a baseline of widely used\nexisting architectures for univariate and multivariate timeseries. The proposed\nmodel appears to outperform the baselines in almost every case of the\nmultivariate timeseries datasets, in some cases even with 50% improvement which\nshows the strengths of such a hybrid architecture in complex timeseries.","url_abs":"http://arxiv.org/abs/1903.02540v1","url_pdf":"http://arxiv.org/pdf/1903.02540v1.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":"autoregressive-convolutional-recurrent-neural","repo_url":"https://github.com/lorenzflow/Autoregressive-Convolutional-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"autoregressive-convolutional-recurrent-neural","repo_url":"https://github.com/xinzezhang/timeseriesforecasting-torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"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"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.02540","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}