{"id":"W7082251863","doi":"10.5281/zenodo.17157421","title":"FORECASTING ELECTRICITY PRICES IN CANADA: A COMPARATIVE ANALYSIS OF ARIMA, LSTM, AND XGBOOST MODELS FOR FINANCIAL DECISION-MAKING","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Niagara","funders":"","keywords":"Autoregressive integrated moving average; Electricity price forecasting; Electricity; Mean absolute percentage error; Gradient boosting; Mean squared error; Electricity market; Renewable energy; Electricity price","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002958655,0.0008858762,0.00067705,0.00120723,0.0009134076,0.001608308,0.001510851,0.0008418218,0.001283485],"category_scores_gemma":[0.006215517,0.0003207609,0.0006334308,0.001934036,0.0004947206,0.0008836928,0.0004361269,0.0009232312,0.0002841032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01097271,"about_ca_system_score_gemma":0.0100511,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9259005,"about_ca_topic_score_gemma":0.8680584,"domain_scores_codex":[0.999334,0.0001863906,0.00003448728,0.0001114924,0.0002189465,0.0001147009],"domain_scores_gemma":[0.996958,0.001374322,0.0001351671,0.00009762066,0.00127714,0.0001578588],"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":[0.0008865367,0.0002326841,0.04783446,0.0001799859,0.0002775468,0.00009674312,0.000102486,0.8864925,0.0005934174,0.002072612,0.005402976,0.05582806],"study_design_scores_gemma":[0.00002641106,0.00005011035,0.009678969,0.00001632539,0.00003934336,0.000007289174,0.00007613196,0.9887536,0.0003631289,0.0003689286,0.0006056371,0.00001415891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682747,0.002721591,0.01627151,0.001988962,0.0001449777,0.00008224256,0.002118531,0.0008601525,0.007537418],"genre_scores_gemma":[0.9889358,0.0006646041,0.006622382,0.00009372363,0.00001986006,0.00002035029,0.001723744,0.00004816783,0.001871346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07409954,"threshold_uncertainty_score":0.1490718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03940236909334296,"score_gpt":0.2512599070635645,"score_spread":0.2118575379702216,"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."}}