{"id":"W1997199759","doi":"10.5539/jmr.v2n4p111","title":"Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model","year":2010,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Artificial neural network; Mean absolute error; Absolute deviation; Mathematics; Exchange rate; Statistics; Us dollar; Econometrics; Pound (networking); Foreign exchange; Approximation error; Artificial intelligence; Computer science; Economics","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":[0.0004821327,0.0003971879,0.0003962511,0.0008278054,0.0002084981,0.0006381007,0.0004787835,0.0004087345,0.0005433101],"category_scores_gemma":[0.001149647,0.0001225575,0.0003732622,0.000984853,0.0001271168,0.0003934675,0.0003935062,0.000513836,0.0001019744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004501787,"about_ca_system_score_gemma":0.0004387194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01930372,"about_ca_topic_score_gemma":0.01568125,"domain_scores_codex":[0.9998266,0.0000550868,0.00001617356,0.00003151634,0.00004359674,0.00002693523],"domain_scores_gemma":[0.999665,0.0001739728,0.00004995391,0.00002002893,0.00007422887,0.00001684819],"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":[0.0002405384,0.0001608974,0.05270138,0.0001851713,0.0001775196,0.0007064847,0.0002043542,0.8805562,0.002030338,0.002133406,0.002179232,0.05872448],"study_design_scores_gemma":[0.000007456099,0.00004536146,0.008433226,0.00001210071,0.00003274304,0.00006570845,0.0000812613,0.9896252,0.000762724,0.0004713656,0.0004519675,0.00001094457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9505847,0.000816734,0.03776487,0.00069984,0.0001171816,0.00004198407,0.0004992786,0.0003419923,0.009133425],"genre_scores_gemma":[0.993059,0.0003076046,0.005343872,0.0000178674,0.00002099187,0.00001075533,0.0002333852,0.00000728117,0.0009992339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01930372,"threshold_uncertainty_score":0.03838271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4487515755731387,"score_gpt":0.5227797922347571,"score_spread":0.07402821666161846,"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."}}