{"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/error-quantified-conformal-inference-for-time","title":"Error-quantified Conformal Inference for Time Series","arxiv_id":"2502.00818","date":"2025-02-02","proceeding":null,"authors":["Junxi Wu","Dongjian Hu","Yajie Bao","Shu-Tao Xia","Changliang Zou"],"abstract":"Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal inference provides a pivotal and flexible instrument for assessing the uncertainty of machine learning models through prediction sets. Recently, a series of online conformal inference methods updated thresholds of prediction sets by performing online gradient descent on a sequence of quantile loss functions. A drawback of such methods is that they only use the information of revealed non-conformity scores via miscoverage indicators but ignore error quantification, namely the distance between the non-conformity score and the current threshold. To accurately leverage the dynamic of miscoverage error, we propose \\textit{Error-quantified Conformal Inference} (ECI) by smoothing the quantile loss function. ECI introduces a continuous and adaptive feedback scale with the miscoverage error, rather than simple binary feedback in existing methods. We establish a long-term coverage guarantee for ECI under arbitrary dependence and distribution shift. The extensive experimental results show that ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.","url_abs":"https://arxiv.org/abs/2502.00818v1","url_pdf":"https://arxiv.org/pdf/2502.00818v1.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":"error-quantified-conformal-inference-for-time","repo_url":"https://github.com/creator-xi/Error-quantified-Conformal-Inference","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2502.00818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.00818"}},"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/creator-xi/Error-quantified-Conformal-Inference","reach":null}],"summary":{"ran_fixture":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"ran":1,"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":0,"samples":[{"code_sha256_prefix":"fec695f08564f558","entry":"ECI","repo":"creator-xi/Error-quantified-Conformal-Inference","repo_kind":"official","path":"core/methods.py","file_url":"https://github.com/creator-xi/Error-quantified-Conformal-Inference/blob/HEAD/core/methods.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fec695f08564f558"}},{"code_sha256_prefix":"7df926179da3c4e5","entry":"aci","repo":"creator-xi/error-quantified-conformal-inference","repo_kind":"official","path":"core/methods.py","file_url":"https://github.com/creator-xi/error-quantified-conformal-inference/blob/HEAD/core/methods.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7df926179da3c4e5"}},{"code_sha256_prefix":"143b959fdfab2d0e","entry":"aci_clipped","repo":"creator-xi/error-quantified-conformal-inference","repo_kind":"official","path":"core/methods.py","file_url":"https://github.com/creator-xi/error-quantified-conformal-inference/blob/HEAD/core/methods.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"143b959fdfab2d0e"}},{"code_sha256_prefix":"4f7ccd99911b6f86","entry":"trailing_window","repo":"creator-xi/error-quantified-conformal-inference","repo_kind":"official","path":"core/methods.py","file_url":"https://github.com/creator-xi/error-quantified-conformal-inference/blob/HEAD/core/methods.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4f7ccd99911b6f86"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}