{"id":"W4395098224","doi":"10.1007/978-981-97-2266-2_20","title":"Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Interpretability; Computer science; Representation (politics); Kernel (algebra); Time series; Series (stratigraphy); Machine learning; Artificial intelligence; AKA; Regression; Data mining; Kernel regression; Statistics; Mathematics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007033172,0.0005267094,0.0005965639,0.0006612616,0.0004554834,0.001905922,0.001508293,0.0001930093,0.00001051961],"category_scores_gemma":[0.0001163839,0.0004253213,0.0002162353,0.0008462589,0.0004670332,0.001927448,0.0009669546,0.0006766494,0.0000274396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526545,"about_ca_system_score_gemma":0.0002844593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001986856,"about_ca_topic_score_gemma":0.00008853523,"domain_scores_codex":[0.996308,0.0000204714,0.0005278246,0.001762029,0.0007240389,0.0006576246],"domain_scores_gemma":[0.9979265,0.0004729401,0.0004128811,0.0008361317,0.0002486724,0.0001028852],"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":[0.0000629978,0.00001963345,0.00007589479,0.0002382523,0.0001204768,0.0002098449,0.00190719,0.3542458,0.0003979779,0.03589843,0.00002658873,0.606797],"study_design_scores_gemma":[0.000142308,0.0003539504,0.00001383513,0.0007252908,0.00003723766,0.000169957,0.000001278225,0.9247056,0.0002934052,0.07225269,0.0007617581,0.0005427149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001731557,0.0003760222,0.9947332,0.0006585633,0.0005083659,0.0004050675,0.000007285035,0.000232549,0.002905796],"genre_scores_gemma":[0.1164177,0.00004421594,0.8365806,0.0002562101,0.0007712508,0.00004997257,0.00007115134,0.0001607777,0.0456481],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6062543,"threshold_uncertainty_score":0.9998199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428187374642939,"score_gpt":0.2342220967053137,"score_spread":0.2199402229588844,"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."}}