{"id":"W2745162628","doi":"10.1142/s2345748117500105","title":"Co-movement and Forecasting Analysis of Major Real Estate Markets by Wavelet Coherence and Multiple Wavelet Coherence","year":2017,"lang":"en","type":"article","venue":"Chinese Journal of Urban and Environmental Studies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wavelet; Coherence (philosophical gambling strategy); Real estate; Econometrics; Wavelet transform; Financial economics; China; Economics; Computer science; Geography; Statistics; Mathematics; Artificial intelligence; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002588418,0.0002182699,0.0005393074,0.00008235638,0.0002520686,0.00004757374,0.0001035329,0.00003858387,0.00001121209],"category_scores_gemma":[0.00006739665,0.0001593448,0.00006650658,0.00004393933,0.0003010631,0.0002040601,0.0001011075,0.0001289219,1.314599e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003562053,"about_ca_system_score_gemma":0.000002353124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005820029,"about_ca_topic_score_gemma":0.00007171847,"domain_scores_codex":[0.9990676,0.00002094798,0.0004136763,0.0001421789,0.0001759858,0.0001795887],"domain_scores_gemma":[0.9992703,0.000162868,0.0003298295,0.0001193204,0.00001129805,0.0001064394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006666011,0.00003838543,0.9406091,0.0001306645,0.001765113,0.00002808369,0.002234717,0.0003784452,0.02119734,0.000001618892,0.00042478,0.03312508],"study_design_scores_gemma":[0.001456995,0.0001975221,0.9715788,0.0001702163,0.0004419484,0.00002953815,0.001262874,0.02151247,0.002667782,0.00004619063,0.0003184461,0.0003172276],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944292,0.004442151,0.00002663895,0.00002143885,0.00007861268,0.00005497938,0.0001131944,0.000006071053,0.0008277537],"genre_scores_gemma":[0.986205,0.01324338,0.0004102354,0.00001053335,0.00003876308,0.000001635719,0.000009520861,0.00001369813,0.0000671957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03280785,"threshold_uncertainty_score":0.6497893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496201453121669,"score_gpt":0.2287495116816336,"score_spread":0.2137874971504169,"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."}}