{"id":"W2018323679","doi":"10.1111/1467-9892.00303","title":"SEARCHING FOR ADDITIVE OUTLIERS IN NONSTATIONARY TIME SERIES*","year":2003,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Outlier; Unit root; Robustness (evolution); Mathematics; Series (stratigraphy); Null hypothesis; Statistical hypothesis testing; Anomaly detection; Statistics; Sample size determination; Time series; Iterative method; Econometrics; Algorithm; Computer science; Data mining","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":[0.007140108,0.0004914386,0.001056776,0.001608374,0.0007151898,0.001259583,0.001045552,0.001043375,0.00183897],"category_scores_gemma":[0.0474282,0.0002995292,0.0006658092,0.001844491,0.001416286,0.001586701,0.001326027,0.001681127,0.0003074111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005565375,"about_ca_system_score_gemma":0.0005082202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008114746,"about_ca_topic_score_gemma":0.0007045614,"domain_scores_codex":[0.9963876,0.001687359,0.0002415475,0.000532023,0.0009912916,0.0001600944],"domain_scores_gemma":[0.9621402,0.02959519,0.003109491,0.002706415,0.002115775,0.0003329105],"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":[0.001401242,0.0004239238,0.1112635,0.0007225333,0.0008479814,0.001467168,0.001125809,0.2583997,0.02969582,0.1072182,0.003702884,0.4837313],"study_design_scores_gemma":[0.000058799,0.0002376958,0.02295731,0.00004895946,0.00007812501,0.0003807663,0.0002139579,0.8957693,0.01621506,0.06156512,0.002407295,0.00006765748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1914224,0.0003199886,0.8061801,0.0004375955,0.000102437,0.0000470867,0.00009163887,0.0003456431,0.001053161],"genre_scores_gemma":[0.8985553,0.0001000956,0.1003664,0.0001047394,0.00004551416,0.00005504383,0.0001620107,0.00004187657,0.0005690059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007140108,"threshold_uncertainty_score":0.03776097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260523629216578,"score_gpt":0.2297422512729007,"score_spread":0.2036898883512429,"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."}}