{"id":"W4410427524","doi":"10.1109/tai.2025.3570676","title":"Leveraging Long-Term Multivariate History Representation for Time Series Forecasting","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Mitacs","keywords":"Term (time); Multivariate statistics; Series (stratigraphy); Representation (politics); Computer science; Time series; Econometrics; Artificial intelligence; Machine learning; Mathematics; Political science; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004344,0.0007928915,0.000523166,0.0007190611,0.0002047192,0.0005270889,0.0007782477,0.0005237565,0.001173058],"category_scores_gemma":[0.001786542,0.0003061678,0.0005574522,0.001067998,0.0002205441,0.001283219,0.0005347364,0.001025293,0.0004286538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00043514,"about_ca_system_score_gemma":0.0005630165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082019,"about_ca_topic_score_gemma":0.01409889,"domain_scores_codex":[0.9998373,0.00002521738,0.00001298186,0.00005236042,0.00004639672,0.00002574775],"domain_scores_gemma":[0.9996748,0.0001358724,0.00004601194,0.00004704445,0.00007629459,0.00002007758],"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":[0.0001951581,0.00009684424,0.006344778,0.0001198068,0.0001251337,0.0002083757,0.0001265368,0.5755963,0.01097447,0.007027491,0.004966013,0.3942192],"study_design_scores_gemma":[0.000002755583,0.00001257511,0.0005821693,0.000005946516,0.00001190075,0.0000133654,0.00000689483,0.9962746,0.000808752,0.001769855,0.0005048078,0.000006436214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1011406,0.001945917,0.8907994,0.000513585,0.0001958212,0.00003986447,0.0008597605,0.001938218,0.002566947],"genre_scores_gemma":[0.9092132,0.001333866,0.08387978,0.0001998163,0.0001602971,0.0000576772,0.002006931,0.0001269661,0.003021449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01082019,"threshold_uncertainty_score":0.02151442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08926465082639921,"score_gpt":0.3037478091007902,"score_spread":0.214483158274391,"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."}}