{"id":"W2045963734","doi":"10.11113/jt.v70.3510","title":"Time Series Forecasting using Least Square Support Vector Machine for Canadian Lynx Data","year":2014,"lang":"en","type":"article","venue":"Jurnal Teknologi","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknologi Malaysia; Ministério da Ciência, Tecnologia e Inovação; Kementerian Sains, Teknologi dan Inovasi","keywords":"Autoregressive integrated moving average; Support vector machine; Artificial neural network; Mean squared error; Time series; Series (stratigraphy); Mean absolute percentage error; Computer science; Flexibility (engineering); Artificial intelligence; Machine learning; Autoregressive model; Data mining; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008023733,0.0005174502,0.0005024298,0.001148524,0.0006228078,0.000686687,0.0006205905,0.0004461069,0.0008473574],"category_scores_gemma":[0.003219603,0.0001681659,0.0004980991,0.002130188,0.0001852376,0.0005245493,0.0003322167,0.0006156402,0.0001643089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890461,"about_ca_system_score_gemma":0.002997401,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.552,"about_ca_topic_score_gemma":0.3725542,"domain_scores_codex":[0.9996953,0.00004022941,0.00002361067,0.00005710243,0.0001388282,0.00004501907],"domain_scores_gemma":[0.9994302,0.000201237,0.0000585641,0.00003195851,0.0002524699,0.00002546235],"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":[0.0003587142,0.0001293367,0.0319601,0.0002916102,0.0001289771,0.0004165116,0.0002321646,0.6629184,0.005745605,0.002493512,0.006167033,0.289158],"study_design_scores_gemma":[0.000004181756,0.00001457804,0.00673871,0.000006889436,0.000009542822,0.00001150446,0.00004070561,0.991688,0.0007108385,0.0002114011,0.0005521388,0.00001157941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8833194,0.002067789,0.1073265,0.0007488348,0.0001613151,0.00005867521,0.001837409,0.001130832,0.003349305],"genre_scores_gemma":[0.9757999,0.0005420641,0.02046391,0.00002829359,0.00002069914,0.00002392135,0.001688498,0.00002250645,0.001410322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.448,"threshold_uncertainty_score":0.9012768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07531674695125204,"score_gpt":0.2790768582223104,"score_spread":0.2037601112710584,"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."}}