{"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-neural-networks","title":"Autoregressive Convolutional Neural Networks for Asynchronous Time Series","arxiv_id":"1703.04122","date":"2017-03-12","proceeding":"ICML 2018 7","authors":["Mikołaj Bińkowski","Gautier Marti","Philippe Donnat"],"abstract":"We propose Significance-Offset Convolutional Neural Network, a deep\nconvolutional network architecture for regression of multivariate asynchronous\ntime series. The model is inspired by standard autoregressive (AR) models and\ngating mechanisms used in recurrent neural networks. It involves an AR-like\nweighting system, where the final predictor is obtained as a weighted sum of\nadjusted regressors, while the weights are datadependent functions learnt\nthrough a convolutional network. The architecture was designed for applications\non asynchronous time series and is evaluated on such datasets: a hedge fund\nproprietary dataset of over 2 million quotes for a credit derivative index, an\nartificially generated noisy autoregressive series and UCI household\nelectricity consumption dataset. The proposed architecture achieves promising\nresults as compared to convolutional and recurrent neural networks.","url_abs":"http://arxiv.org/abs/1703.04122v4","url_pdf":"http://arxiv.org/pdf/1703.04122v4.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-neural-networks","repo_url":"https://github.com/mbinkowski/nntimeseries","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"autoregressive-convolutional-neural-networks","repo_url":"https://github.com/Fangyh09/Autoregressive-Convolutional-Neural-Networks.Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04122","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}