{"id":"W4206748003","doi":"10.1007/s10489-021-02926-x","title":"Time Series Reconstruction and Classification: A Comprehensive Comparative Study","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Curse of dimensionality; Series (stratigraphy); Computer science; Representation (politics); Dimensionality reduction; Time series; Cluster analysis; Feature vector; Pattern recognition (psychology); Algorithm; Artificial intelligence; Approximation error; Space (punctuation); Feature (linguistics); Data mining; 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":[0.009988996,0.0005875108,0.001087634,0.007254483,0.0004784863,0.003242369,0.0009672279,0.0009922122,0.002027178],"category_scores_gemma":[0.01833595,0.0002932504,0.001260216,0.006644911,0.0009935631,0.004439888,0.0007925636,0.0007160208,0.0005345171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008428977,"about_ca_system_score_gemma":0.0009887114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002938088,"about_ca_topic_score_gemma":0.002830296,"domain_scores_codex":[0.997306,0.001119171,0.0002082139,0.0002626686,0.0009809675,0.0001229886],"domain_scores_gemma":[0.9798858,0.01523917,0.00111172,0.001305826,0.002281357,0.0001761837],"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":[0.0006350953,0.0002470086,0.04106078,0.0008208205,0.0004723501,0.0001654527,0.0006186921,0.008416154,0.002217182,0.01850889,0.003115179,0.9237223],"study_design_scores_gemma":[0.000125504,0.002344834,0.2972421,0.002857161,0.003697186,0.00393455,0.007782044,0.4317844,0.02448755,0.1186898,0.1066433,0.0004116617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4058877,0.2896356,0.2497322,0.005940649,0.0006171654,0.0002592005,0.0008933262,0.0005743731,0.04645985],"genre_scores_gemma":[0.8636976,0.07334688,0.05736006,0.0002996653,0.0004968405,0.00005163613,0.0009323708,0.0001076139,0.00370732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009988996,"threshold_uncertainty_score":0.05282748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05185108909975805,"score_gpt":0.2671468358803061,"score_spread":0.215295746780548,"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."}}