{"id":"W4408107795","doi":"10.1140/epjqt/s40507-025-00333-6","title":"QSegRNN: quantum segment recurrent neural network for time series forecasting","year":2025,"lang":"en","type":"article","venue":"EPJ Quantum Technology","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut canadien d'information sur la santé; Korea Agency for Infrastructure Technology Advancement; National Research Foundation of Korea","keywords":"Series (stratigraphy); Artificial neural network; Time series; Quantum; Computer science; Statistical physics; Artificial intelligence; Physics; Machine learning; Geology; Quantum mechanics; Paleontology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004604553,0.0004821801,0.0005071297,0.0002556052,0.0002384201,0.0004018429,0.001097635,0.0005452237,0.001860029],"category_scores_gemma":[0.001166699,0.0002224785,0.0004403373,0.0004134568,0.0003129098,0.0008548892,0.0003760924,0.0008607063,0.0003503808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006852,"about_ca_system_score_gemma":0.00067692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01159007,"about_ca_topic_score_gemma":0.01115872,"domain_scores_codex":[0.9998362,0.00003432122,0.00001143212,0.00004005105,0.00005464768,0.00002334988],"domain_scores_gemma":[0.999798,0.00007831003,0.00002508394,0.00002695648,0.00006058744,0.0000111188],"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":[0.0001563164,0.00008021049,0.001198913,0.000106788,0.0000953834,0.0001155186,0.00004552625,0.8644195,0.01095292,0.01472249,0.004322192,0.1037843],"study_design_scores_gemma":[0.000002017755,0.000008087393,0.00005704224,0.000001622521,0.00000403375,0.000004451875,0.000001121364,0.9978893,0.0006414859,0.001130775,0.0002576425,0.000002419787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08739924,0.002367395,0.8985794,0.0008329984,0.0002767882,0.00006897759,0.0004760266,0.002786382,0.0072128],"genre_scores_gemma":[0.8940378,0.001054687,0.09824807,0.0002514361,0.00005922904,0.00008511122,0.0009007666,0.0001190841,0.00524372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01159007,"threshold_uncertainty_score":0.02304518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09176718026237213,"score_gpt":0.3842497319201791,"score_spread":0.292482551657807,"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."}}