{"id":"W4396620509","doi":"10.1016/j.eswa.2024.124129","title":"Integrating deep transformer and temporal convolutional networks for SMEs revenue and employment growth prediction","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"","keywords":"Computer science; Revenue; Transformer; Artificial intelligence; Convolutional neural network; Machine learning; Finance; Business; Electrical engineering","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":[0.0005571332,0.0005728659,0.0003842917,0.0007360931,0.000164085,0.0005338585,0.0007319301,0.0005050802,0.001787299],"category_scores_gemma":[0.001082937,0.000216308,0.0004676344,0.0007413289,0.0001321801,0.0008753851,0.0005088797,0.0008069436,0.0007517354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005932401,"about_ca_system_score_gemma":0.000802053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.015715,"about_ca_topic_score_gemma":0.02683173,"domain_scores_codex":[0.9998924,0.00001576984,0.000006403164,0.000030923,0.00002323913,0.00003124038],"domain_scores_gemma":[0.9997383,0.00009040446,0.00002874488,0.00002613535,0.00009128051,0.00002515138],"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":[0.0004676181,0.0004401593,0.01696282,0.0001198255,0.000208189,0.0002008088,0.00006346668,0.3776562,0.01099352,0.006827057,0.01056258,0.5754977],"study_design_scores_gemma":[0.000002786412,0.00001132895,0.0007014722,0.000004376147,0.00001307374,0.00001012205,0.000004752139,0.996613,0.0009500282,0.001414839,0.000271413,0.000002870293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3965834,0.003267302,0.5837278,0.001270603,0.0004043963,0.00006230387,0.002128582,0.003709109,0.008846519],"genre_scores_gemma":[0.9632766,0.0006269107,0.02974832,0.0001228602,0.00008075924,0.00002487402,0.001355945,0.000044059,0.004719549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.015715,"threshold_uncertainty_score":0.03124708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05361556026075728,"score_gpt":0.3618383054063002,"score_spread":0.3082227451455429,"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."}}