{"id":"W4388207260","doi":"10.1109/ccece58730.2023.10289082","title":"On the Performance of Legendre State-Space Models in Short-Term Time Series Forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Term (time); Series (stratigraphy); Legendre polynomials; State space; Computer science; Time series; State (computer science); Applied mathematics; Mathematics; Algorithm; Physics; Mathematical analysis; Machine learning; Statistics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009284008,0.0001744133,0.0003284285,0.0004420934,0.0001458553,0.00009656263,0.0008516329,0.0000563656,0.0004802671],"category_scores_gemma":[0.003716664,0.0001001142,0.0000907054,0.002211255,0.0001409749,0.000488504,0.0003345634,0.0002025715,0.000206943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003294016,"about_ca_system_score_gemma":0.00006658908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000181889,"about_ca_topic_score_gemma":0.00005400656,"domain_scores_codex":[0.9968876,0.0004113319,0.0006787315,0.0004259122,0.0011851,0.0004113248],"domain_scores_gemma":[0.9919082,0.007054425,0.0001192923,0.0006813467,0.0001791149,0.0000576315],"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":[0.001148865,0.0001411795,0.128729,0.00008279002,0.0000661415,0.00007969833,0.01303255,0.4334655,0.008754041,0.02513544,0.03289283,0.3564721],"study_design_scores_gemma":[0.0001305104,0.0001972836,0.02053548,0.00008329453,0.000003024157,0.00001171909,0.0005930363,0.9371892,0.01044674,0.03051804,0.0001078663,0.0001838366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9601271,0.000007726048,0.00156695,0.0004814738,0.0001621373,0.0002544577,0.000009932083,0.00008408983,0.03730611],"genre_scores_gemma":[0.9789523,0.000006889781,0.003694083,0.00004244989,0.00002071031,0.00002259359,0.000001456359,0.00002032805,0.01723912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5037237,"threshold_uncertainty_score":0.5258588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1880095110883207,"score_gpt":0.3713388512740289,"score_spread":0.1833293401857082,"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."}}