{"id":"W2375364090","doi":"","title":"Time Series Prediction Based on Incremental Pruning Least Square Support Vector Machine","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Support vector machine; Pruning; Least squares support vector machine; Series (stratigraphy); Algorithm; Block (permutation group theory); Matrix (chemical analysis); Kernel (algebra); Function (biology); Least-squares function approximation; Time series; Relevance vector machine; Artificial intelligence; Machine learning; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000714764,0.0004282997,0.001017083,0.0007054032,0.0003095578,0.0005292963,0.001100706,0.0006050908,0.0007930607],"category_scores_gemma":[0.004689967,0.0002564809,0.0004050477,0.0008063625,0.0002363843,0.001001918,0.0004826041,0.000735903,0.0003174113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002648055,"about_ca_system_score_gemma":0.0006210373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003975512,"about_ca_topic_score_gemma":0.002831319,"domain_scores_codex":[0.9994243,0.0001096689,0.00002871309,0.0000878138,0.0002969689,0.00005251468],"domain_scores_gemma":[0.998808,0.0005336974,0.00009087929,0.00009989145,0.0004299034,0.0000376087],"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":[0.0002473043,0.0001433761,0.002677596,0.0001068908,0.00009285733,0.0002907386,0.0001021562,0.2837857,0.02385898,0.004555869,0.003629325,0.6805092],"study_design_scores_gemma":[0.000005743362,0.00002574819,0.0003868179,0.000002517051,0.000008385394,0.00003517273,0.000003341069,0.9969311,0.001743431,0.0005995904,0.0002533231,0.000004827704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05197737,0.0003653985,0.9453448,0.0001129982,0.00007091778,0.00003238281,0.00005892021,0.001079299,0.0009578067],"genre_scores_gemma":[0.7116424,0.0004737706,0.2850121,0.00008268905,0.00009511263,0.0001101549,0.0003888377,0.00009284008,0.00210199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003975512,"threshold_uncertainty_score":0.007904768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003664760564758694,"score_gpt":0.1984586298706792,"score_spread":0.1947938693059205,"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."}}