{"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/deepar-probabilistic-forecasting-with","title":"DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks","arxiv_id":"1704.04110","date":"2017-04-13","proceeding":null,"authors":["David Salinas","Valentin Flunkert","Jan Gasthaus"],"abstract":"Probabilistic forecasting, i.e. estimating the probability distribution of a\ntime series' future given its past, is a key enabler for optimizing business\nprocesses. In retail businesses, for example, forecasting demand is crucial for\nhaving the right inventory available at the right time at the right place. In\nthis paper we propose DeepAR, a methodology for producing accurate\nprobabilistic forecasts, based on training an auto regressive recurrent network\nmodel on a large number of related time series. We demonstrate how by applying\ndeep learning techniques to forecasting, one can overcome many of the\nchallenges faced by widely-used classical approaches to the problem. We show\nthrough extensive empirical evaluation on several real-world forecasting data\nsets accuracy improvements of around 15% compared to state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1704.04110v3","url_pdf":"http://arxiv.org/pdf/1704.04110v3.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":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/Nixtla/neuralforecast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/Timbasa/Sample_GluonTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/Yonder-OSS/D3M-Primitives","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/alphaj-jaeminyx/DeepAR-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/bingblackbean/water_supply_network_pressure_pred_deepar","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/dhopp1/nowcasting_benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/eeci/annex_37","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/husnejahan/DeepAR-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/kshmawj111/solar_energy_forecast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/ledererlab/deepcar","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/nuankw/Summer-Research-2018-Part-One","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/potosnakw/neuralforecast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/rekahalmai/ImpliedVolatilityPrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/skp2/Electricity-Load","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/ucl-exoplanets/deepARTransit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/xinzezhang/timeseriesforecasting-torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/zhykoties/DeepAR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/etna-team/etna","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deepar-probabilistic-forecasting-with","repo_url":"https://github.com/jdb78/pytorch-forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"probabilistic-time-series-forecasting","task_name":"Probabilistic Time Series Forecasting"},{"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.04110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.04110"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Timbasa/Sample_GluonTS","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nuankw/Summer-Research-2018-Part-One","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/skp2/Electricity-Load","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/etna-team/etna","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Nixtla/neuralforecast","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/bingblackbean/water_supply_network_pressure_pred_deepar","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dhopp1/nowcasting_benchmark","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/alphaj-jaeminyx/DeepAR-keras","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ledererlab/deepcar","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kshmawj111/solar_energy_forecast","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xinzezhang/timeseriesforecasting-torch","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jdb78/pytorch-forecasting","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/husnejahan/DeepAR-pytorch","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Yonder-OSS/D3M-Primitives","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/potosnakw/neuralforecast","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rekahalmai/ImpliedVolatilityPrediction","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/eeci/annex_37","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zhykoties/DeepAR","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ucl-exoplanets/deepARTransit","reach":{"status":"unanswered"}}],"summary":{"ran_draft_wrong":2,"ran_fixture":1},"by_repo_kind":{"listed":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"8252d4bcce662261","entry":"get_metrics","repo":"xinzezhang/timeseriesforecasting-torch","repo_kind":"listed","path":"models/training/deepAR.py","file_url":"https://github.com/xinzezhang/timeseriesforecasting-torch/blob/HEAD/models/training/deepAR.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8252d4bcce662261"}},{"code_sha256_prefix":"1ac937a48e910d3d","entry":"init_metrics","repo":"xinzezhang/timeseriesforecasting-torch","repo_kind":"listed","path":"models/training/deepAR.py","file_url":"https://github.com/xinzezhang/timeseriesforecasting-torch/blob/HEAD/models/training/deepAR.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1ac937a48e910d3d"}},{"code_sha256_prefix":"28bb99867acbde6e","entry":"update_metrics","repo":"xinzezhang/timeseriesforecasting-torch","repo_kind":"listed","path":"models/training/deepAR.py","file_url":"https://github.com/xinzezhang/timeseriesforecasting-torch/blob/HEAD/models/training/deepAR.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"28bb99867acbde6e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}