{"id":"W2886443255","doi":"10.5430/ijba.v9n5p11","title":"Forecasting the Daily Transaction Data Utilizing a Day of the Week Index in the Case of Web Site","year":2018,"lang":"en","type":"article","venue":"International Journal of Business Administration","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exponential smoothing; Smoothing; Computer science; Constant (computer programming); Variance (accounting); Database transaction; Index (typography); Moving average; Autoregressive–moving-average model; Data mining; Simple (philosophy); Mathematical optimization; Econometrics; Algorithm; Mathematics; Database; Autoregressive model","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":[0.0007749489,0.0004294657,0.0004452969,0.001470997,0.0002788148,0.0008561319,0.0006154247,0.0007284707,0.000862335],"category_scores_gemma":[0.003098857,0.0001683692,0.000485794,0.0023196,0.0001916368,0.001478177,0.0002475723,0.000838288,0.0003890238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003755573,"about_ca_system_score_gemma":0.000445384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00841367,"about_ca_topic_score_gemma":0.009787262,"domain_scores_codex":[0.9995824,0.00008299006,0.00003791389,0.0001133583,0.0001405908,0.00004285984],"domain_scores_gemma":[0.9991302,0.000341038,0.0001160391,0.0001279216,0.0002412005,0.00004370657],"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":[0.0003759508,0.0003368687,0.1691878,0.0004856404,0.0002333697,0.0005533344,0.0004908128,0.2659857,0.01441452,0.01453361,0.01327606,0.5201263],"study_design_scores_gemma":[0.0000124631,0.00008725435,0.03193043,0.00003033347,0.00005854534,0.0001229882,0.000230325,0.9513403,0.005124629,0.005439612,0.005575825,0.0000472605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4833668,0.001871229,0.5010411,0.001209941,0.0004144155,0.0001288988,0.002245825,0.001169647,0.008552083],"genre_scores_gemma":[0.9299893,0.0009321002,0.06417987,0.00006817208,0.0001143511,0.00006222489,0.001695321,0.00005081114,0.002907834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00841367,"threshold_uncertainty_score":0.01672941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2418043447938453,"score_gpt":0.4163174426379792,"score_spread":0.1745130978441339,"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."}}