{"id":"W4386196482","doi":"10.1016/j.engappai.2023.106892","title":"Adaptive error bounded piecewise linear approximation for time-series representation","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bounded function; Representation (politics); Approximation error; Series (stratigraphy); Piecewise; Algorithm; Set (abstract data type); Process (computing); Piecewise linear function; Time series; Mathematical optimization; Applied mathematics; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000285642,0.0001073027,0.0001593062,0.0002191797,0.0001396612,0.00006255065,0.0004083055,0.00004567471,0.000009164854],"category_scores_gemma":[0.0001387179,0.0001169155,0.00009242968,0.001458095,0.00004194985,0.0003716846,0.00008576078,0.0000594435,0.0001108577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002349318,"about_ca_system_score_gemma":0.00002541982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001606061,"about_ca_topic_score_gemma":0.00000293669,"domain_scores_codex":[0.9989377,0.000009819232,0.0004060864,0.0002954969,0.0001612052,0.0001897483],"domain_scores_gemma":[0.9990173,0.0001654285,0.0001446768,0.0004180704,0.0002096955,0.00004483161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001698086,0.00004924199,0.000005276565,0.00004289503,0.00003981099,3.171528e-7,0.0007903465,0.3961591,0.01358134,0.4329179,0.000111267,0.1562855],"study_design_scores_gemma":[0.00001122407,0.00004507532,0.00002369637,0.00001118401,0.000009707668,8.561044e-7,0.0001603999,0.9278423,0.05628711,0.01463419,0.0008667182,0.0001075141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001626718,0.00002318095,0.9971755,0.0002712352,0.00005890418,0.0004318988,0.00001255021,0.000311464,0.00008855882],"genre_scores_gemma":[0.3528235,0.00001359699,0.6458919,0.000007730512,0.0001573163,0.0006514267,0.00007134824,0.00002293694,0.000360207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5316832,"threshold_uncertainty_score":0.4767677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04637178431607342,"score_gpt":0.2927483634917176,"score_spread":0.2463765791756442,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}