{"id":"W2533706881","doi":"10.1109/iceas.2011.6147107","title":"Price forecasting using computational intelligence techniques: A comparative analysis","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Support vector machine; Particle swarm optimization; Volatility (finance); Electricity market; Computational intelligence; Robustness (evolution); Electricity price forecasting; Electricity; Mathematical optimization; Swarm intelligence; Interpretability; Artificial intelligence; Machine learning; Econometrics; Economics; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001583534,0.0004519107,0.0005620348,0.004441134,0.0002461171,0.00123623,0.0005086733,0.0006169204,0.001686728],"category_scores_gemma":[0.005396079,0.0001266193,0.0007023471,0.004950475,0.0002322679,0.00121857,0.0003107026,0.0003785368,0.0003621046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006244536,"about_ca_system_score_gemma":0.0003640059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002372199,"about_ca_topic_score_gemma":0.001773153,"domain_scores_codex":[0.9989371,0.0002960879,0.00006194809,0.00005514013,0.0005987183,0.00005098534],"domain_scores_gemma":[0.9959046,0.002938391,0.0001778072,0.0001846922,0.0007445292,0.00005000888],"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":[0.0007244179,0.0004486377,0.02783928,0.0009198714,0.0006045717,0.0002963656,0.0001947869,0.1685177,0.002639425,0.01361381,0.005253632,0.7789475],"study_design_scores_gemma":[0.0000595667,0.0007633581,0.05925008,0.0002211897,0.0004422729,0.0002925529,0.0003155415,0.9076847,0.005930848,0.007636643,0.01733555,0.00006755601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6894853,0.05642819,0.11289,0.002495387,0.0005612827,0.0002404904,0.0008955169,0.0006264129,0.1363774],"genre_scores_gemma":[0.9482052,0.02032442,0.02791508,0.0000710126,0.0002061759,0.00006322976,0.0006174017,0.00004542609,0.002552071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004441134,"threshold_uncertainty_score":0.008374631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1160960358292929,"score_gpt":0.2811831970404434,"score_spread":0.1650871612111504,"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."}}