{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009995969,0.0005469999,0.0007756663,0.0003798248,0.0001764168,0.0007789677,0.0008941525,0.0008659038,0.001497508],"category_scores_gemma":[0.005258148,0.0002813146,0.0005428499,0.0007074033,0.0004428795,0.0007701636,0.0007248904,0.001771246,0.0005545246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004306673,"about_ca_system_score_gemma":0.000458581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003318128,"about_ca_topic_score_gemma":0.001414029,"domain_scores_codex":[0.9996153,0.0001553831,0.00001989526,0.00006133803,0.0001146181,0.00003345489],"domain_scores_gemma":[0.9985261,0.001020938,0.00008670249,0.0001297434,0.0002070034,0.0000295258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001827427,0.00004317545,0.0004734586,0.0002808965,0.00006506626,0.0001054391,0.0001077412,0.7957184,0.01055548,0.0535192,0.001916512,0.1370317],"study_design_scores_gemma":[0.000001183597,0.000006953469,0.0000305174,0.000003308716,0.000003380642,0.000006242454,0.000001642742,0.9976163,0.0002845261,0.001802328,0.0002418998,0.000001758952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003310678,0.0003315585,0.9956675,0.00005832369,0.00004208673,0.000006428581,0.00002109709,0.000143477,0.0004188769],"genre_scores_gemma":[0.6770316,0.001988171,0.3100832,0.0001090816,0.0001622951,0.0001779965,0.0003150166,0.000220791,0.009911889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003318128,"threshold_uncertainty_score":0.006597638,"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."}}