{"id":"W1991384011","doi":"10.1109/ccece.2008.4564858","title":"An algorithm for the identification of autoregressive moving average systems from noisy observations","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Autoregressive–moving-average model; Autoregressive model; Noise (video); Autocorrelation; Residual; Algorithm; Estimation theory; System identification; Moving average; Mathematics; Computer science; Least-squares function approximation; SIGNAL (programming language); Statistics; Artificial intelligence; Data modeling; Estimator","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001040569,0.001070219,0.001070967,0.001039977,0.0007907064,0.0007913584,0.001442508,0.001373736,0.003024085],"category_scores_gemma":[0.003526643,0.0006299759,0.0008181729,0.0009791114,0.000588852,0.001247712,0.001080082,0.002015101,0.002423285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004317749,"about_ca_system_score_gemma":0.0009275285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001493298,"about_ca_topic_score_gemma":0.001608331,"domain_scores_codex":[0.999352,0.0001379896,0.00005506994,0.000168933,0.0002520294,0.00003390869],"domain_scores_gemma":[0.9992387,0.0003467772,0.00008083587,0.00009409313,0.0002213276,0.000018249],"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":[0.0001706574,0.00007236722,0.0004838108,0.0002386037,0.0001254213,0.0001653607,0.0001580383,0.1796605,0.01890902,0.05168616,0.004600937,0.7437291],"study_design_scores_gemma":[0.00003447225,0.00006530352,0.0003118548,0.00003335638,0.00002670927,0.0002028309,0.0000169281,0.9626289,0.006384701,0.01662428,0.01363372,0.00003693233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003279783,0.00006435865,0.9991955,0.00001520237,0.00002561635,0.00001265289,0.00001040525,0.0001682678,0.000180034],"genre_scores_gemma":[0.01524036,0.000152175,0.9827069,0.00003367308,0.00004945772,0.0001662841,0.0001253262,0.00007957745,0.001446256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003024085,"threshold_uncertainty_score":0.01011652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02886718657785391,"score_gpt":0.2238233891140005,"score_spread":0.1949562025361466,"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."}}