{"id":"W3122967885","doi":"10.2139/ssrn.720441","title":"Using Self-Organizing Maps to Adjust Intra-day Seasonality","year":2005,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Seasonality; Smoothing; Econometrics; Volatility (finance); Artificial neural network; Component (thermodynamics); Computer science; Nonlinear system; Mathematics; Statistics; Artificial intelligence","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":[0.0009124928,0.0004687162,0.0004703875,0.001138329,0.0003060212,0.0008285263,0.0004981013,0.0004889514,0.0006142406],"category_scores_gemma":[0.003159016,0.0003191491,0.0005392935,0.001141212,0.0002179135,0.0007914976,0.0003745718,0.0005650268,0.0002553707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003144406,"about_ca_system_score_gemma":0.0003506488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005557188,"about_ca_topic_score_gemma":0.007136455,"domain_scores_codex":[0.9998115,0.00004644741,0.00001113532,0.0000637266,0.00003914268,0.00002823227],"domain_scores_gemma":[0.9990721,0.000510702,0.0000683467,0.00008536424,0.0002305738,0.00003284964],"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":[0.0005219969,0.000305685,0.01675999,0.0001415139,0.0003572725,0.0001072689,0.0003370686,0.4302704,0.01395108,0.001678954,0.00617538,0.5293934],"study_design_scores_gemma":[0.00001153066,0.00001855746,0.004140885,0.000003644033,0.00001833145,0.00001251623,0.00002448641,0.9924644,0.001596887,0.00137135,0.0003257083,0.0000116385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3442812,0.0005556254,0.648143,0.0002470791,0.0003894679,0.00008270448,0.0004075824,0.003095393,0.002797943],"genre_scores_gemma":[0.9036862,0.0001413428,0.0945124,0.00004911264,0.00008396988,0.00004020467,0.0003810957,0.000167431,0.0009383455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005557188,"threshold_uncertainty_score":0.01104969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01569258778596685,"score_gpt":0.2385151043986362,"score_spread":0.2228225166126693,"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."}}