{"id":"W4411120930","doi":"10.1111/coin.70073","title":"An Innovative Sentiment Influenced Stock Market Prediction Based on Dual Scale Adaptive Residual Long Short Term Memory With Attention Mechanism","year":2025,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Residual; Dual (grammatical number); Term (time); Computer science; Stock market; Mechanism (biology); Artificial intelligence; Stock (firearms); Econometrics; Cognitive psychology; Economics; Algorithm; Psychology; Engineering; History; Linguistics","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":[0.0003947391,0.0006349827,0.0008122238,0.0006029981,0.0003296065,0.0006660739,0.001181356,0.0006243346,0.002334033],"category_scores_gemma":[0.0005895667,0.0002956536,0.0005852914,0.0004063871,0.0002031169,0.0007446457,0.0005477716,0.0006231415,0.0004497624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005282787,"about_ca_system_score_gemma":0.0007387538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01316796,"about_ca_topic_score_gemma":0.01196451,"domain_scores_codex":[0.9998091,0.00001529629,0.00001178885,0.00005652762,0.00006047376,0.00004693738],"domain_scores_gemma":[0.9998043,0.00003682089,0.00002595193,0.00001195452,0.00009866042,0.00002226112],"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":[0.0006984697,0.0007118522,0.01334509,0.0001653355,0.0002814044,0.0006035449,0.00009929154,0.3646965,0.03795249,0.003901962,0.008194699,0.5693494],"study_design_scores_gemma":[0.000009046411,0.00003942224,0.0004716762,0.000002746849,0.0000164632,0.00001356683,0.000002824338,0.9979714,0.001109676,0.0002177218,0.000141071,0.00000439455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2980058,0.002390491,0.6851601,0.0009928785,0.0004140293,0.0001491396,0.00030106,0.003045771,0.009540697],"genre_scores_gemma":[0.970028,0.000301657,0.02475211,0.0002267029,0.00008271947,0.00005042709,0.0001947299,0.00001945161,0.004344344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01316796,"threshold_uncertainty_score":0.02618259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07485950746457086,"score_gpt":0.3950558657720371,"score_spread":0.3201963583074662,"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."}}