{"id":"W4321474754","doi":"10.5281/zenodo.7659860","title":"Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Period (music); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Stock market; Series (stratigraphy); Time series; Economics; Virology; History; Medicine; Mathematics; Statistics; Geology; Philosophy; Internal medicine; Archaeology","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":[0.0004221132,0.0002061428,0.0001964404,0.000946012,0.0001833681,0.0004847899,0.0001994798,0.0002868451,0.001253197],"category_scores_gemma":[0.001377365,0.00005827776,0.000207074,0.000776754,0.0001004397,0.0004127946,0.0002182144,0.0003741939,0.0002102003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003627934,"about_ca_system_score_gemma":0.000227995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079127,"about_ca_topic_score_gemma":0.0121408,"domain_scores_codex":[0.9998648,0.00001504023,0.00001083452,0.0000341359,0.00005150948,0.00002366551],"domain_scores_gemma":[0.999571,0.00009309919,0.00008917489,0.00002367121,0.000171925,0.00005117426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002899925,0.0002786553,0.8796185,0.00007244036,0.0001738143,0.001100054,0.0004096772,0.02412047,0.006687168,0.002196849,0.007708082,0.07734429],"study_design_scores_gemma":[0.000008694748,0.0001045926,0.8387053,0.00002318237,0.00006279228,0.000125284,0.0004505854,0.1556995,0.001753583,0.0009427222,0.00209891,0.00002486913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969704,0.0001511563,0.0007626091,0.0001992189,0.00003220302,0.000007966149,0.0008497785,0.00002949262,0.000997121],"genre_scores_gemma":[0.9977778,0.0000878195,0.0003464202,0.0000265747,0.00002321619,0.000007019234,0.001133413,0.00000347784,0.0005944217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01079127,"threshold_uncertainty_score":0.0214569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1221768267388586,"score_gpt":0.3769134119064345,"score_spread":0.2547365851675759,"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."}}