{"id":"W2724638838","doi":"10.1109/bigmm.2017.76","title":"Trend and Value Based Time Series Representation for Similarity Search","year":2017,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Nearest neighbor search; Preprocessor; Data mining; Similarity (geometry); Curse of dimensionality; Series (stratigraphy); Representation (politics); Transformation (genetics); Process (computing); Time series; Similarity measure; Artificial intelligence; Value (mathematics); Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002800066,0.00005982293,0.000104203,0.00003480411,0.0005853273,0.0006132119,0.0003263688,0.00002596982,0.00003738026],"category_scores_gemma":[0.00006917648,0.00004949261,0.00004873884,0.00004823443,0.00005972268,0.0007568832,0.0001711139,0.00003162025,0.000003704292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006140117,"about_ca_system_score_gemma":0.0000147373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001325284,"about_ca_topic_score_gemma":0.00005637652,"domain_scores_codex":[0.9993918,0.00002020936,0.00009907227,0.0002412877,0.0001095005,0.0001381443],"domain_scores_gemma":[0.9993082,0.00006524038,0.00005746046,0.0004861219,0.00003422296,0.00004873567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001620383,0.0001548275,0.02180768,0.0001267638,0.0001760518,0.00001877312,0.001696895,0.003230519,0.006734762,0.3083237,0.007958365,0.6496096],"study_design_scores_gemma":[0.0002387197,0.00007258467,0.01318619,0.000004752188,0.0000088614,0.000002082856,0.00002473539,0.9782166,0.004345384,0.001954227,0.001855675,0.0000901798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03836236,0.00003096941,0.9333915,0.008767656,0.00009956421,0.0002568566,0.00001579178,0.0001318538,0.01894349],"genre_scores_gemma":[0.7457969,0.000004330023,0.2469815,0.0001456573,0.00007748764,0.00001084613,0.00001360952,0.000007333088,0.006962374],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9749861,"threshold_uncertainty_score":0.5913216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04540092119491593,"score_gpt":0.3042654850254244,"score_spread":0.2588645638305084,"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."}}