{"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/adaptive-conformal-predictions-for-time","title":"Adaptive Conformal Predictions for Time Series","arxiv_id":"2202.07282","date":"2022-02-15","proceeding":null,"authors":["Margaux Zaffran","Aymeric Dieuleveut","Olivier Féron","Yannig Goude","Julie Josse"],"abstract":"Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency. We theoretically analyse the impact of the learning rate on its efficiency in the exchangeable and auto-regressive case. We propose a parameter-free method, AgACI, that adaptively builds upon ACI based on online expert aggregation. We lead extensive fair simulations against competing methods that advocate for ACI's use in time series. We conduct a real case study: electricity price forecasting. The proposed aggregation algorithm provides efficient prediction intervals for day-ahead forecasting. All the code and data to reproduce the experiments is made available.","url_abs":"https://arxiv.org/abs/2202.07282v1","url_pdf":"https://arxiv.org/pdf/2202.07282v1.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":"adaptive-conformal-predictions-for-time","repo_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"adaptive-conformal-predictions-for-time","repo_url":"https://github.com/shai128/rrc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"conformal-prediction","task_name":"Conformal Prediction"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"prediction-intervals","task_name":"Prediction Intervals"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2202.07282","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.07282"}},"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/mzaffran/adaptiveconformalpredictionstimeseries","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/shai128/rrc","reach":{"status":"ok"}}],"summary":{"unverified":11},"by_repo_kind":{"official":{"samples":11,"ran":0,"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":"92458f3fcaf4fcdd","entry":"compute_true_length","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"utils.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/utils.py","link_basis":"harvester_set","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":"92458f3fcaf4fcdd"}},{"code_sha256_prefix":"b11bdb4a461f92fa","entry":"fit_predict_ACPs","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"models.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/models.py","link_basis":"harvester_set","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":"b11bdb4a461f92fa"}},{"code_sha256_prefix":"4960f1aafb7e7eb9","entry":"generate_data","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"generation.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/generation.py","link_basis":"harvester_set","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":"4960f1aafb7e7eb9"}},{"code_sha256_prefix":"25a807154f5daa9c","entry":"generate_multiple_data","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"generation.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/generation.py","link_basis":"harvester_set","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":"25a807154f5daa9c"}},{"code_sha256_prefix":"d67fa66de54d00f0","entry":"get_name_data","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"files.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/files.py","link_basis":"harvester_set","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":"d67fa66de54d00f0"}},{"code_sha256_prefix":"4db8481a6bb9f816","entry":"get_name_results","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"files.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/files.py","link_basis":"harvester_set","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":"4db8481a6bb9f816"}},{"code_sha256_prefix":"87829a399740dc06","entry":"load_file","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"files.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/files.py","link_basis":"harvester_set","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":"87829a399740dc06"}},{"code_sha256_prefix":"fc6a001fbd90a494","entry":"missing_data","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"enbpi/utils_EnbPI.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/enbpi/utils_EnbPI.py","link_basis":"harvester_set","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":"fc6a001fbd90a494"}},{"code_sha256_prefix":"c94b9c51276f663a","entry":"read_CA_data","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"enbpi/utils_EnbPI.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/enbpi/utils_EnbPI.py","link_basis":"harvester_set","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":"c94b9c51276f663a"}},{"code_sha256_prefix":"d05a83ba6296f8f0","entry":"read_data","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"enbpi/utils_EnbPI.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/enbpi/utils_EnbPI.py","link_basis":"harvester_set","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":"d05a83ba6296f8f0"}},{"code_sha256_prefix":"66b18bf3288051bc","entry":"read_pickle","repo":"mzaffran/adaptiveconformalpredictionstimeseries","repo_kind":"official","path":"AgACI/R/utils.py","file_url":"https://github.com/mzaffran/adaptiveconformalpredictionstimeseries/blob/HEAD/AgACI/R/utils.py","link_basis":"harvester_set","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":"66b18bf3288051bc"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}