{"id":"W4319297879","doi":"10.26420/austinjmedoncol.2022.1073","title":"Application of Deep Learning LSTM and ARIMA Models in Time Series Forecasting: A Methods Case Study analyzing Canadian and Swedish Indoor Air Pollution Data","year":2022,"lang":"en","type":"article","venue":"Austin Journal of Medical Oncology","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Autoregressive integrated moving average; Time series; Series (stratigraphy); Computer science; Deep learning; Air pollution; Meteorology; Pollution; Artificial intelligence; Machine learning; Econometrics; Geography; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008742557,0.00008718474,0.000312314,0.0001481955,0.0002479518,0.000009349959,0.0003166539,0.0000761249,0.00009148949],"category_scores_gemma":[0.00116651,0.00008321284,0.00001720117,0.0002844782,0.0002049772,0.0002274322,0.0006577394,0.0007210223,3.136252e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003289736,"about_ca_system_score_gemma":0.0001293786,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03247374,"about_ca_topic_score_gemma":0.01300803,"domain_scores_codex":[0.9976699,0.0008807685,0.0005723814,0.0002120022,0.0004494616,0.0002154908],"domain_scores_gemma":[0.9987,0.000445884,0.0004249096,0.0001203562,0.00001645847,0.0002923575],"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":[0.00009730061,0.0002096013,0.3086296,0.00001762805,0.00003816168,0.002078766,0.006699988,0.02283794,0.0001428683,0.00001320111,0.00005962639,0.6591753],"study_design_scores_gemma":[0.002078169,0.002707269,0.04770914,0.0000482108,0.0001567117,0.02374266,0.02857799,0.8910111,0.00001541954,0.000578707,0.003136594,0.0002380144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862715,0.0001303725,0.01190368,0.001408021,0.00007996637,0.0001292484,0.000003742202,0.000004530427,0.00006896571],"genre_scores_gemma":[0.9876263,0.00001331805,0.01220476,0.00005751147,0.00006890973,0.000006048429,0.000003370593,0.000007542,0.00001226255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8681732,"threshold_uncertainty_score":0.9739691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07139141615744923,"score_gpt":0.3695365913958433,"score_spread":0.2981451752383941,"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."}}