{"id":"W2770605332","doi":"10.1002/cjas.1465","title":"Business Forecasting of Double‐trend Time Series: An Improved PLS‐based Time‐varying Weight Combination Approach","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Series (stratigraphy); Time series; Volatility (finance); Partial least squares regression; Data mining; Econometrics; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002043094,0.001337633,0.001174787,0.001446402,0.0004148075,0.001034478,0.001246304,0.0008605665,0.001632191],"category_scores_gemma":[0.003119713,0.000623061,0.00165029,0.002008392,0.0003283829,0.001464622,0.0008961047,0.001389078,0.000622701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362391,"about_ca_system_score_gemma":0.0007763474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003407025,"about_ca_topic_score_gemma":0.00285171,"domain_scores_codex":[0.998893,0.0003676356,0.00007123884,0.0002750401,0.0003251442,0.00006792752],"domain_scores_gemma":[0.998798,0.0005771413,0.0001271663,0.000119815,0.0003328732,0.00004498904],"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":[0.0002066892,0.0002221219,0.003584774,0.0001162609,0.0003729874,0.0001342409,0.0001144181,0.6880919,0.00800579,0.003097128,0.001954153,0.2940995],"study_design_scores_gemma":[0.000003237987,0.00001798342,0.0002703681,0.000002320795,0.00001512007,0.000008790421,0.00000490104,0.9982979,0.0004165971,0.0007092668,0.0002475546,0.000005984333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03792678,0.0002477636,0.9599546,0.0001643682,0.00004564432,0.00004419002,0.000133406,0.0005444947,0.0009387545],"genre_scores_gemma":[0.6423599,0.0004101463,0.351942,0.0001401577,0.000148176,0.0001780028,0.0008960538,0.0001852748,0.003740272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003407025,"threshold_uncertainty_score":0.01080507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.255647218686247,"score_gpt":0.3818559899545343,"score_spread":0.1262087712682873,"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."}}