diff --git a/benchmarks/fev_bench/models.yaml b/benchmarks/fev_bench/models.yaml index 506e305..5eb14db 100644 --- a/benchmarks/fev_bench/models.yaml +++ b/benchmarks/fev_bench/models.yaml @@ -332,3 +332,29 @@ timersi-1_5: model_type: closed-api zero_shot: false commercial_use: false + +pdm-femto: + organization: Flashmind UAE + url: https://www.flashmind.com/models/pdm#pdm-femto + model_type: closed-api + zero_shot: true + commercial_use: true + display_name: PDM-Femto + hidden: true + +pdm-pico: + organization: Flashmind UAE + url: https://www.flashmind.com/models/pdm#pdm-pico + model_type: closed-api + zero_shot: true + commercial_use: true + display_name: PDM-Pico + hidden: true + +pdm-nano: + organization: Flashmind UAE + url: https://www.flashmind.com/models/pdm#pdm-nano + model_type: closed-api + zero_shot: true + commercial_use: true + display_name: PDM-Nano diff --git a/benchmarks/fev_bench/results/pdm-femto.csv b/benchmarks/fev_bench/results/pdm-femto.csv new file mode 100644 index 0000000..065b15d --- /dev/null +++ b/benchmarks/fev_bench/results/pdm-femto.csv @@ -0,0 +1,101 @@ +model_name,dataset_path,dataset_config,horizon,num_windows,initial_cutoff,window_step_size,min_context_length,max_context_length,seasonality,eval_metric,extra_metrics,quantile_levels,id_column,timestamp_column,target,generate_univariate_targets_from,known_dynamic_columns,past_dynamic_columns,static_columns,task_name,description_fingerprint,test_error,training_time_s,inference_time_s,num_forecasts,dataset_fingerprint,trained_on_this_dataset,fev_version,SQL,MASE,WAPE,WQL,model_class,model_kwargs +PDM-Femto,autogluon/fev_datasets,proenfo_gfc12,168,10,-1680,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc12,,0.5909520589657193,0.0,2.785395946004428,110,70e6cce11b73757a,False,0.11.0.dev0,0.5909520589657193,0.7524761138317612,0.06179000343353926,0.048388831657696106,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,proenfo_gfc14,168,20,-3360,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc14,,0.3804171965751252,0.0,1.7935541059996467,20,87312edc76952605,False,0.11.0.dev0,0.3804171965751252,0.4808594645913783,0.023660329845675176,0.01871059947605335,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,proenfo_gfc17,168,20,-3360,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc17,,0.4250911466827281,0.0,2.5053550159937004,160,897258c227dde589,False,0.11.0.dev0,0.4250911466827281,0.5280692614697494,0.03425581262613159,0.027563076219367592,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,rohlik_sales_1D,14,1,2023-12-15T00:00:00,14,14,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,sales,,"['holiday', 'school_holidays', 'sell_price_main', 'shops_closed', 'total_orders', 'type_0_discount', 'type_1_discount', 'type_2_discount', 'type_3_discount', 'type_4_discount', 'type_5_discount', 'type_6_discount', 'winter_school_holidays']",['availability'],"['L1_category_name_en', 'L2_category_name_en', 'L3_category_name_en', 'L4_category_name_en', 'name', 'product_unique_id', 'warehouse']",rohlik_sales_1D,,0.92963414381107,0.0,25.98385615000734,4116,2c9ecdd477948cce,False,0.11.0.dev0,0.92963414381107,1.1593932637434214,0.2901079281767239,0.23121776352655196,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,rohlik_orders_1D,61,5,2023-05-01T00:00:00,61,1,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,orders,,"['holiday', 'school_holidays', 'shops_closed', 'winter_school_holidays']","['blackout', 'frankfurt_shutdown', 'mini_shutdown', 'mov_change', 'precipitation', 'shutdown', 'snow', 'user_activity_1', 'user_activity_2']",[],rohlik_orders_1D,,0.9508929534660107,0.0,1.0675949709984707,35,d4ccf61e0ac3ccdf,False,0.11.0.dev0,0.9508929534660107,1.1625500788095278,0.05363797554620479,0.04395690490716722,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,rohlik_sales_1W,8,1,2023-12-15T00:00:00,8,8,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,sales,,"['holiday', 'school_holidays', 'sell_price_main', 'shops_closed', 'total_orders', 'type_0_discount', 'type_1_discount', 'type_2_discount', 'type_3_discount', 'type_4_discount', 'type_5_discount', 'type_6_discount', 'winter_school_holidays']",['availability'],"['L1_category_name_en', 'L2_category_name_en', 'L3_category_name_en', 'L4_category_name_en', 'name', 'product_unique_id', 'warehouse']",rohlik_sales_1W,,1.1106188410782225,0.0,60.34587480100163,3942,149ec34eb852d454,False,0.11.0.dev0,1.1106188410782225,1.374287917862327,0.20031887065586926,0.15929264836646123,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,rohlik_orders_1W,8,5,2023-05-01T00:00:00,8,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,orders,,"['holiday', 'school_holidays', 'shops_closed', 'winter_school_holidays']","['blackout', 'frankfurt_shutdown', 'mini_shutdown', 'mov_change', 'precipitation', 'shutdown', 'snow', 'user_activity_1', 'user_activity_2']",[],rohlik_orders_1W,,1.3445384537424538,0.0,1.6493188559979899,35,4eea90b920dec215,False,0.11.0.dev0,1.3445384537424538,1.6553925938859932,0.0541338671762863,0.043925074024414576,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,entsoe_15T,96,20,-1920,96,1,,96,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['radiation_diffuse_horizontal', 'radiation_direct_horizontal', 'temperature']",[],[],entsoe_15T,,0.44790293832185946,0.0,3.96162302599987,120,8facdab2c9ff5bd5,False,0.11.0.dev0,0.44790293832185946,0.5653714043282679,0.040914195630924854,0.032278317771087986,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,entsoe_30T,96,20,-1920,96,1,,48,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['radiation_diffuse_horizontal', 'radiation_direct_horizontal', 'temperature']",[],[],entsoe_30T,,0.4414339624683478,0.0,3.435152709003887,120,98ab81cde986fe22,False,0.11.0.dev0,0.4414339624683478,0.5504042928229971,0.03622806757262591,0.029404563113532144,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,entsoe_1H,168,20,-3360,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['radiation_diffuse_horizontal', 'radiation_direct_horizontal', 'temperature']",[],[],entsoe_1H,,0.3872368568198986,0.0,3.93613858599565,120,c1b23331fc6093bc,False,0.11.0.dev0,0.3872368568198986,0.47995611538545707,0.030501037467994672,0.024572134921246012,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,epf_be,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Generation forecast', 'System load forecast']",[],[],epf_be,,0.48470959263456537,0.0,2.006021352004609,20,2b9df413e5ec356e,False,0.11.0.dev0,0.48470959263456537,0.6171963285622095,0.11089386745048044,0.08706740066707666,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,epf_de,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Ampirion Load Forecast', 'PV+Wind Forecast']",[],[],epf_de,,0.5236876275422379,0.0,2.295798676001141,20,bc6dede7cf6a507b,False,0.11.0.dev0,0.5236876275422379,0.6594311093906307,0.30942966249004344,0.24486155897933778,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,epf_fr,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Generation forecast', 'System load forecast']",[],[],epf_fr,,0.3564505435552197,0.0,1.4890081100020325,20,a00b09fd79e0529e,False,0.11.0.dev0,0.3564505435552197,0.4451933634575977,0.06780723879431885,0.054278992505456904,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,epf_np,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Grid load forecast', 'Wind power forecast']",[],[],epf_np,,0.7203239252178012,0.0,2.140785269002663,20,9830dda233defc6c,False,0.11.0.dev0,0.7203239252178012,0.901282922258137,0.040123237920393996,0.03199123106005675,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,epf_pjm,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['System load forecast', 'Zonal COMED load foecast']",[],[],epf_pjm,,0.39120793914522267,0.0,1.7694567930011544,20,9228310bf7192a0a,False,0.11.0.dev0,0.39120793914522267,0.4897561915194393,0.08534545758592971,0.0682037033497051,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,rossmann_1D,48,10,-480,48,1,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,Sales,,"['DayOfWeek', 'Open', 'Promo', 'SchoolHoliday', 'StateHoliday']",['Customers'],"['Assortment', 'CompetitionDistance', 'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval', 'Store', 'StoreType']",rossmann_1D,,0.2537707531194434,0.0,52.14935091898951,11150,83a79b0a9a999f56,False,0.11.0.dev0,0.2537707531194434,0.31518587964836653,0.10521414126268722,0.08482946063312032,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,rossmann_1W,13,8,-104,13,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,Sales,,"['Open', 'Promo', 'SchoolHoliday', 'StateHoliday']",['Customers'],"['Assortment', 'CompetitionDistance', 'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval', 'Store', 'StoreType']",rossmann_1W,,0.2597963271793917,0.0,28.310967083991272,8920,28f46b06b50c6d84,False,0.11.0.dev0,0.2597963271793917,0.31025314085206784,0.08031257704665216,0.06742064145720535,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,restaurant,28,8,-224,28,1,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],"['air_area_name', 'air_genre_name', 'latitude', 'longitude']",restaurant,,0.6784192751372778,0.0,16.385301863992936,6502,eb8da1769444e614,False,0.11.0.dev0,0.6784192751372778,0.847833702155856,0.3578829464473118,0.28193472448742757,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,hermes,52,1,-52,52,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['external'],[],"['category', 'country']",hermes,,0.6195601500004723,0.0,30.08552118499938,10000,003815c209114c79,False,0.11.0.dev0,0.6195601500004723,0.7891449723151425,0.002927730113197514,0.002295301863000879,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,walmart,39,1,-39,39,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['CPI', 'Fuel_Price', 'IsHoliday', 'MarkDown1', 'MarkDown2', 'MarkDown3', 'MarkDown4', 'MarkDown5', 'Temperature', 'Unemployment']",[],"['Dept', 'Size', 'Store', 'Type']",walmart,,0.6906790380491783,0.0,13.494677429989679,2936,5de0711be36aab0b,False,0.11.0.dev0,0.6906790380491783,0.8696161520496665,0.10090938523587945,0.08017063698418798,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,m5_1D,28,1,-28,28,1,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['event_Cultural', 'event_National', 'event_Religious', 'event_Sporting', 'sell_price', 'snap_CA', 'snap_TX', 'snap_WI']",[],"['cat_id', 'dept_id', 'item_id', 'state_id', 'store_id']",m5_1D,,0.7051290079965847,0.0,192.63982165000925,30490,91e836c1eb428f2d,False,0.11.0.dev0,0.7051290079965847,0.8636303690667302,0.691503504070319,0.5435726890975219,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": ""46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27""}" +PDM-Femto,autogluon/fev_datasets,m5_1W,13,1,-13,13,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['event_Cultural', 'event_National', 'event_Religious', 'event_Sporting', 'sell_price', 'snap_CA', 'snap_TX', 'snap_WI']",[],"['cat_id', 'dept_id', 'item_id', 'state_id', 'store_id']",m5_1W,,0.8798128153904146,0.0,128.69166744498943,30490,bb4e7816349be551,False,0.11.0.dev0,0.8798128153904146,1.120965072361104,0.41684756660244143,0.3268686842351864,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-femto"", ""revision"": 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a/benchmarks/fev_bench/results/pdm-nano.csv b/benchmarks/fev_bench/results/pdm-nano.csv new file mode 100644 index 0000000..3479bd5 --- /dev/null +++ b/benchmarks/fev_bench/results/pdm-nano.csv @@ -0,0 +1,101 @@ +model_name,dataset_path,dataset_config,horizon,num_windows,initial_cutoff,window_step_size,min_context_length,max_context_length,seasonality,eval_metric,extra_metrics,quantile_levels,id_column,timestamp_column,target,generate_univariate_targets_from,known_dynamic_columns,past_dynamic_columns,static_columns,task_name,description_fingerprint,test_error,training_time_s,inference_time_s,num_forecasts,dataset_fingerprint,trained_on_this_dataset,fev_version,SQL,MASE,WAPE,WQL,model_class,model_kwargs +PDM-Nano,autogluon/fev_datasets,proenfo_gfc12,168,10,-1680,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc12,,0.523596512595325,0.0,14.698016966009163,110,70e6cce11b73757a,False,0.11.0.dev0,0.523596512595325,0.6636836548597023,0.05222931265767269,0.041327397356203506,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,proenfo_gfc14,168,20,-3360,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc14,,0.2874396593721882,0.0,4.5216843290108955,20,87312edc76952605,False,0.11.0.dev0,0.2874396593721882,0.36557151937213206,0.01801567387223569,0.01415043816938821,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" 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""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,epf_be,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Generation forecast', 'System load forecast']",[],[],epf_be,,0.45323780303224925,0.0,2.1610953000053996,20,2b9df413e5ec356e,False,0.11.0.dev0,0.45323780303224925,0.5793336018522665,0.10298089951521554,0.08043589900657311,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,epf_de,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Ampirion Load Forecast', 'PV+Wind Forecast']",[],[],epf_de,,0.44701863416441523,0.0,2.3120866550016217,20,bc6dede7cf6a507b,False,0.11.0.dev0,0.44701863416441523,0.5656674636775434,0.3207860957301506,0.2519547439500032,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,epf_fr,24,20,-480,24,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['Generation forecast', 'System load forecast']",[],[],epf_fr,,0.30373096761522644,0.0,3.486444569003652,20,a00b09fd79e0529e,False,0.11.0.dev0,0.30373096761522644,0.38669307928160246,0.05840699981212107,0.046019250278904515,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,epf_np,24,20,-480,24,1,,24,SQL,"['MASE', 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'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['CPI', 'Fuel_Price', 'IsHoliday', 'MarkDown1', 'MarkDown2', 'MarkDown3', 'MarkDown4', 'MarkDown5', 'Temperature', 'Unemployment']",[],"['Dept', 'Size', 'Store', 'Type']",walmart,,0.683680403090388,0.0,143.72085822299414,2936,5de0711be36aab0b,False,0.11.0.dev0,0.683680403090388,0.8586158771204969,0.09945027141960744,0.07906777639229182,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,m5_1D,28,1,-28,28,1,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['event_Cultural', 'event_National', 'event_Religious', 'event_Sporting', 'sell_price', 'snap_CA', 'snap_TX', 'snap_WI']",[],"['cat_id', 'dept_id', 'item_id', 'state_id', 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0.9]",id,timestamp,"['AAA', 'AWHMAN', 'BAA', 'BUSINVx', 'CPIAUCSL', 'CPIMEDSL', 'CUMFNS', 'DMANEMP', 'EXUSUKx', 'FEDFUNDS', 'GS1', 'GS10', 'GS5', 'HOUST', 'INDPRO', 'INVEST', 'IPBUSEQ', 'IPCONGD', 'IPDCONGD', 'IPDMAT', 'IPFINAL', 'IPMANSICS', 'IPMAT', 'IPNCONGD', 'IPNMAT', 'M2REAL', 'MANEMP', 'NDMANEMP', 'NONBORRES', 'PAYEMS', 'PERMIT', 'PPICMM', 'S&P 500', 'SRVPRD', 'TB3MS', 'TB6MS', 'TOTRESNS', 'UEMP15T26', 'UEMP27OV', 'UEMP5TO14', 'UEMPLT5', 'UEMPMEAN', 'UMCSENTx', 'UNRATE', 'USCONS', 'USFIRE', 'USGOOD', 'USGOVT', 'USTPU', 'USTRADE', 'VIXCLSx']",,[],[],[],fred_qd_2025/macro,,3.302206708244191,0.0,28.35411742600263,1020,d4ca154cc310644e,False,0.11.0.dev0,3.302206708244191,3.99772242671414,0.10045861730355816,0.08145238222547996,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,us_consumption_1M,12,10,-120,12,1,,12,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],[],us_consumption_1M,,1.4568850852908297,0.0,4.195094766007969,310,dee11a908eddc948,False,0.11.0.dev0,1.4568850852908297,1.7605388751777955,0.02267405253248434,0.019622304878099107,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,us_consumption_1Q,8,10,-80,8,1,,4,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],[],us_consumption_1Q,,1.5893568582412674,0.0,4.109464643988758,310,05aa7957a46336f0,False,0.11.0.dev0,1.5893568582412674,1.9799872457549292,0.030022628514163246,0.024882586020895943,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,us_consumption_1Y,5,10,-50,5,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],[],us_consumption_1Y,,3.464428718337836,0.0,4.127265333008836,310,34316f35c64781aa,False,0.11.0.dev0,3.464428718337836,4.294478714804181,0.052199658535515024,0.040687390998300876,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" +PDM-Nano,autogluon/fev_datasets,gvar,8,10,-80,8,1,,4,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['Dp', 'ep', 'eq', 'lr', 'r', 'y']",,[],"['pmat', 'pmetal', 'poil']",[],gvar,,0.5515691601148192,0.0,31.02292297000531,1980,d404e6be8d6f82fd,False,0.11.0.dev0,0.5515691601148192,0.6763507883304156,0.017747390301878403,0.014401342027499594,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-nano"", ""revision"": ""48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27""}" diff --git a/benchmarks/fev_bench/results/pdm-pico.csv b/benchmarks/fev_bench/results/pdm-pico.csv new file mode 100644 index 0000000..3673007 --- /dev/null +++ b/benchmarks/fev_bench/results/pdm-pico.csv @@ -0,0 +1,101 @@ +model_name,dataset_path,dataset_config,horizon,num_windows,initial_cutoff,window_step_size,min_context_length,max_context_length,seasonality,eval_metric,extra_metrics,quantile_levels,id_column,timestamp_column,target,generate_univariate_targets_from,known_dynamic_columns,past_dynamic_columns,static_columns,task_name,description_fingerprint,test_error,training_time_s,inference_time_s,num_forecasts,dataset_fingerprint,trained_on_this_dataset,fev_version,SQL,MASE,WAPE,WQL,model_class,model_kwargs +PDM-Pico,autogluon/fev_datasets,proenfo_gfc12,168,10,-1680,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc12,,0.574102442923088,0.0,4.30969447499956,110,70e6cce11b73757a,False,0.11.0.dev0,0.574102442923088,0.7329956707578804,0.057902904883192885,0.045599465208874965,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": 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0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc17,,0.3975746369053946,0.0,4.934412636997877,160,897258c227dde589,False,0.11.0.dev0,0.3975746369053946,0.4971825022832551,0.03204308771019506,0.025657181687615705,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,rohlik_sales_1D,14,1,2023-12-15T00:00:00,14,14,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,sales,,"['holiday', 'school_holidays', 'sell_price_main', 'shops_closed', 'total_orders', 'type_0_discount', 'type_1_discount', 'type_2_discount', 'type_3_discount', 'type_4_discount', 'type_5_discount', 'type_6_discount', 'winter_school_holidays']",['availability'],"['L1_category_name_en', 'L2_category_name_en', 'L3_category_name_en', 'L4_category_name_en', 'name', 'product_unique_id', 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""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,rohlik_orders_1W,8,5,2023-05-01T00:00:00,8,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,orders,,"['holiday', 'school_holidays', 'shops_closed', 'winter_school_holidays']","['blackout', 'frankfurt_shutdown', 'mini_shutdown', 'mov_change', 'precipitation', 'shutdown', 'snow', 'user_activity_1', 'user_activity_2']",[],rohlik_orders_1W,,1.213413572754723,0.0,1.8773019679938443,35,4eea90b920dec215,False,0.11.0.dev0,1.213413572754723,1.4771123489749909,0.04833147524862047,0.039675937443007764,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,entsoe_15T,96,20,-1920,96,1,,96,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['radiation_diffuse_horizontal', 'radiation_direct_horizontal', 'temperature']",[],[],entsoe_15T,,0.45510487638776764,0.0,3.071733848002623,120,8facdab2c9ff5bd5,False,0.11.0.dev0,0.45510487638776764,0.5839832351269536,0.04346000341155094,0.0336018170050849,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,entsoe_30T,96,20,-1920,96,1,,48,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['radiation_diffuse_horizontal', 'radiation_direct_horizontal', 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1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_28', 'target_29', 'target_3', 'target_30', 'target_31', 'target_32', 'target_33', 'target_34', 'target_35', 'target_36', 'target_37', 'target_38', 'target_39', 'target_4', 'target_5', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1631,,0.5749713532214477,0.0,8.176477667002473,800,74d0d6952f666083,False,0.11.0.dev0,0.5749713532214477,0.7207368480281184,0.3162240775890743,0.25177997226841575,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,boomlet_1676,96,20,-1920,96,1,,48,SQL,"['MASE', 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'target_98', 'target_99']",,[],[],[],boomlet_2187,,0.7134250838745974,0.0,18.026345791004132,2000,a13b0fc5b533c30e,False,0.11.0.dev0,0.7134250838745974,0.8212228203243764,0.2684338028223824,0.23221543992651056,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,boomlet_285,60,20,-1200,60,1,,1440,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_28', 'target_29', 'target_3', 'target_30', 'target_31', 'target_32', 'target_33', 'target_34', 'target_35', 'target_36', 'target_37', 'target_38', 'target_39', 'target_4', 'target_40', 'target_41', 'target_42', 'target_43', 'target_44', 'target_45', 'target_46', 'target_47', 'target_48', 'target_49', 'target_5', 'target_50', 'target_51', 'target_52', 'target_53', 'target_54', 'target_55', 'target_56', 'target_57', 'target_58', 'target_59', 'target_6', 'target_60', 'target_61', 'target_62', 'target_63', 'target_64', 'target_65', 'target_66', 'target_67', 'target_68', 'target_69', 'target_7', 'target_70', 'target_71', 'target_72', 'target_73', 'target_74', 'target_8', 'target_9']",,[],[],[],boomlet_285,,0.3143770916013916,0.0,33.8480613950087,1500,0ced6efaa846e1bd,False,0.11.0.dev0,0.3143770916013916,0.3797879570214639,0.1707838557135352,0.14139362738270603,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,boomlet_619,60,20,-1200,60,1,,1440,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_28', 'target_29', 'target_3', 'target_30', 'target_31', 'target_32', 'target_33', 'target_34', 'target_35', 'target_36', 'target_37', 'target_38', 'target_39', 'target_4', 'target_40', 'target_41', 'target_42', 'target_43', 'target_44', 'target_45', 'target_46', 'target_47', 'target_48', 'target_49', 'target_5', 'target_50', 'target_51', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_619,,0.3283797458158318,0.0,10.721884764003335,1040,83721e76c6faf4a6,False,0.11.0.dev0,0.3283797458158318,0.4288349249314737,0.3047581259124579,0.23488253878220014,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,boomlet_772,60,20,-1200,60,1,,1440,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_28', 'target_29', 'target_3', 'target_30', 'target_31', 'target_32', 'target_33', 'target_34', 'target_35', 'target_36', 'target_37', 'target_38', 'target_39', 'target_4', 'target_40', 'target_41', 'target_42', 'target_43', 'target_44', 'target_45', 'target_46', 'target_47', 'target_48', 'target_49', 'target_5', 'target_50', 'target_51', 'target_52', 'target_53', 'target_54', 'target_55', 'target_56', 'target_57', 'target_58', 'target_59', 'target_6', 'target_60', 'target_61', 'target_62', 'target_63', 'target_64', 'target_65', 'target_66', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_772,,0.2796971325588622,0.0,35.068872761010425,1340,f6a344659a6a948e,False,0.11.0.dev0,0.2796971325588622,0.3289880073263963,0.1549776843143937,0.13079403126036765,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,boomlet_963,60,20,-1200,60,1,,1440,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_3', 'target_4', 'target_5', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_963,,0.694654341573034,0.0,6.422662908997154,560,5bbc9fb121124724,False,0.11.0.dev0,0.694654341573034,0.8066918328246653,0.3753916986763661,0.3121111174033378,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,fred_md_2025,12,20,-240,12,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['CPIAUCSL', 'FEDFUNDS', 'PAYEMS']",,[],"['M2REAL', 'NONBORRES', 'PPICMM', 'TOTRESNS']",[],fred_md_2025/cee,,3.4771600779965355,0.0,1.2732157899881713,60,74fe4767146a4d96,False,0.11.0.dev0,3.4771600779965355,4.271876913378767,0.07441773438472726,0.06341715561021238,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,fred_qd_2025,8,20,-160,8,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['CPIAUCSL', 'FEDFUNDS', 'PAYEMS']",,[],"['M2REAL', 'NONBORRES', 'PPICMM', 'TOTRESNS']",[],fred_qd_2025/cee,,2.080053638045976,0.0,1.402103950997116,60,d4ca154cc310644e,False,0.11.0.dev0,2.080053638045976,2.518453136138082,0.15454940772261466,0.12662550983689952,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,fred_md_2025,12,20,-240,12,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['AAA', 'AWHMAN', 'BAA', 'BUSINVx', 'CPIAUCSL', 'CPIMEDSL', 'CUMFNS', 'DMANEMP', 'EXUSUKx', 'FEDFUNDS', 'GS1', 'GS10', 'GS5', 'HOUST', 'INDPRO', 'INVEST', 'IPBUSEQ', 'IPCONGD', 'IPDCONGD', 'IPDMAT', 'IPFINAL', 'IPMANSICS', 'IPMAT', 'IPNCONGD', 'IPNMAT', 'M2REAL', 'MANEMP', 'NDMANEMP', 'NONBORRES', 'PAYEMS', 'PERMIT', 'PPICMM', 'S&P 500', 'SRVPRD', 'TB3MS', 'TB6MS', 'TOTRESNS', 'UEMP15T26', 'UEMP27OV', 'UEMP5TO14', 'UEMPLT5', 'UEMPMEAN', 'UMCSENTx', 'UNRATE', 'USCONS', 'USFIRE', 'USGOOD', 'USGOVT', 'USTPU', 'USTRADE', 'VIXCLSx']",,[],[],[],fred_md_2025/macro,,5.238769143671268,0.0,5.850332139991224,1020,74fe4767146a4d96,False,0.11.0.dev0,5.238769143671268,6.2359787112388725,0.07745123269477903,0.0631662384705909,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,fred_qd_2025,8,20,-160,8,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['AAA', 'AWHMAN', 'BAA', 'BUSINVx', 'CPIAUCSL', 'CPIMEDSL', 'CUMFNS', 'DMANEMP', 'EXUSUKx', 'FEDFUNDS', 'GS1', 'GS10', 'GS5', 'HOUST', 'INDPRO', 'INVEST', 'IPBUSEQ', 'IPCONGD', 'IPDCONGD', 'IPDMAT', 'IPFINAL', 'IPMANSICS', 'IPMAT', 'IPNCONGD', 'IPNMAT', 'M2REAL', 'MANEMP', 'NDMANEMP', 'NONBORRES', 'PAYEMS', 'PERMIT', 'PPICMM', 'S&P 500', 'SRVPRD', 'TB3MS', 'TB6MS', 'TOTRESNS', 'UEMP15T26', 'UEMP27OV', 'UEMP5TO14', 'UEMPLT5', 'UEMPMEAN', 'UMCSENTx', 'UNRATE', 'USCONS', 'USFIRE', 'USGOOD', 'USGOVT', 'USTPU', 'USTRADE', 'VIXCLSx']",,[],[],[],fred_qd_2025/macro,,3.346320420367755,0.0,5.659187910001492,1020,d4ca154cc310644e,False,0.11.0.dev0,3.346320420367755,4.00551658509903,0.10177597517231354,0.08286151067321815,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,us_consumption_1M,12,10,-120,12,1,,12,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],[],us_consumption_1M,,1.4973873188902427,0.0,1.8157996619993355,310,dee11a908eddc948,False,0.11.0.dev0,1.4973873188902427,1.820078702110872,0.02359954960283834,0.020241108646805513,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,us_consumption_1Q,8,10,-80,8,1,,4,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],[],us_consumption_1Q,,1.6269691908770003,0.0,1.6822201679897262,310,05aa7957a46336f0,False,0.11.0.dev0,1.6269691908770003,2.0126436834624086,0.030198868925161014,0.025173780569917027,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,us_consumption_1Y,5,10,-50,5,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,[],[],[],us_consumption_1Y,,3.3189578035275353,0.0,1.7576593900012085,310,34316f35c64781aa,False,0.11.0.dev0,3.3189578035275353,4.156546758678525,0.05088615657911064,0.039153690781941676,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" +PDM-Pico,autogluon/fev_datasets,gvar,8,10,-80,8,1,,4,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['Dp', 'ep', 'eq', 'lr', 'r', 'y']",,[],"['pmat', 'pmetal', 'poil']",[],gvar,,0.5570525310270039,0.0,14.620089367002947,1980,d404e6be8d6f82fd,False,0.11.0.dev0,0.5570525310270039,0.6817880361820212,0.01781904210651107,0.01454939857521903,flashpdm,"{""concurrency"": 32, ""model"": ""pdm-pico"", ""revision"": ""cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27""}" diff --git a/models/flashpdm/model.py b/models/flashpdm/model.py new file mode 100644 index 0000000..7b8f7ee --- /dev/null +++ b/models/flashpdm/model.py @@ -0,0 +1,178 @@ +"""Flashmind PDM through the public flashpdm client. + +https://www.flashmind.com/models/pdm + +Targets are forecast jointly, including with known-future covariates. +Numeric dynamic covariates retain their roles. String covariates use smoothed +per-series target means fitted on observed history only (one channel per target); +static columns are unused. +""" + +from itertools import tee +from typing import ClassVar + +import datasets +import numpy as np +import pyarrow as pa +from flashpdm import Client +from flashpdm.batching import BatchLimits, ForecastPool + +import fev + +REVISIONS = { + "pdm-femto": "46bb0bde1c836a6b1a82e6d0108f766adb47c8639cf9c695637c583a617fe06c.2026-10-27", + "pdm-pico": "cf380714c0e6eda4a43a1e62a12cf228b3c0795fa21be7078ddaf6b866c501bd.2026-10-27", + "pdm-nano": "48cc3fd4918228d313179822d616fca5c282c5bcc9e59d55bc33c31b2c98eb5e.2026-10-27", +} + + +def _numeric(values): + return np.asarray(values, dtype=np.float64) + + +def _values(values): + a = _numeric(values) + if np.isinf(a).any(): + raise ValueError("Infinite observations are not supported") + return np.where(np.isnan(a), None, a).tolist() + + +def _target_encode(target, past, future=None): + """Smoothed category means (one pseudo-observation); unseen -> history mean.""" + + def key(value): + return None if value is None or value == "" or (isinstance(value, float) and np.isnan(value)) else str(value) + + y = _numeric(target) + observed = y[np.isfinite(y)] + mean = float(observed.mean()) if len(observed) else 0.0 + stats = {} + for value, category in zip(y, past, strict=True): + if np.isfinite(value): + k = key(category) + total, count = stats.get(k, (0.0, 0)) + stats[k] = (total + value, count + 1) + + def encode(values): + result = [] + for value in values: + total, count = stats.get(key(value), (0.0, 0)) + result.append((mean + total) / (1 + count)) + return np.asarray(result, dtype=np.float32) + + return encode(past), None if future is None else encode(future) + + +def _project(quantiles, history): + """Infer nonnegative/count support from the observed history, never labels.""" + observed = _numeric(history) + observed = observed[np.isfinite(observed)] + if len(observed) and (observed >= 0).all(): + quantiles = np.maximum(quantiles, 0) + if len(observed) >= 8 and (observed == np.rint(observed)).all(): + quantiles = np.rint(quantiles) + return quantiles + + +class FlashPDMModel(fev.ForecastingModel): + model_name = "flashpdm" + trained_on_datasets: ClassVar[list[str]] = [] + + def __init__(self, model="pdm-nano", revision=None, concurrency=32, batch_size=16): + super().__init__() + if model not in REVISIONS: + raise ValueError(f"Choose one of {list(REVISIONS)}") + if not 1 <= concurrency <= 256 or not 1 <= batch_size <= 32: + raise ValueError("concurrency must be 1..256 and batch_size 1..32") + self.model = model + self.revision = revision or REVISIONS[model] + self.concurrency = concurrency + self.batch_size = batch_size + + def _items(self, task, windows, categorical, cap): + for w, (past_data, future_data) in enumerate(windows): + for i, (past, future) in enumerate(zip(past_data, future_data, strict=True)): + columns = task.target_columns + targets = {c: _numeric(past[c]) for c in columns} + p, f = {}, {} + reserved = set(columns) | set(task.known_dynamic_columns) | set(task.past_dynamic_columns) + for c in task.known_dynamic_columns + task.past_dynamic_columns: + ahead = future[c] if c in task.known_dynamic_columns else None + variants = ( + [_target_encode(y, past[c], ahead) for y in targets.values()] + if c in categorical + else [(past[c], ahead)] + ) + for j, (hist, known) in enumerate(variants): + name = c + if len(variants) > 1: + name = f"encoded-{len(p)}-{j}" + while name in reserved or name in p: + name = "_" + name + p[name] = _values(_numeric(hist)[-cap:]) + if known is not None: + f[name] = _values(known) + payload = {"targets": {c: _values(y[-cap:]) for c, y in targets.items()}} + if p: + payload["past_covariates"] = p + if f: + payload["future_covariates"] = f + yield (w, i, targets, payload) + + def _fit_predict(self, task): + levels = sorted({0.5, *task.quantile_levels}) + table = task.load_full_dataset(num_proc=1).data.table + categorical = set() + for c in task.dynamic_columns: + dtype = table.schema.field(c).type.value_type + if not (pa.types.is_integer(dtype) or pa.types.is_floating(dtype) or pa.types.is_boolean(dtype)): + categorical.add(c) + windows = [window.get_input_data() for window in task.iter_windows()] + bands = [ + {c: np.full((len(past), task.horizon, len(levels)), np.nan) for c in task.target_columns} + for past, _ in windows + ] + + with ( + Client(timeout=180, max_retries=2) as client, + ForecastPool( + client, + model=self.model, + revision=self.revision, + concurrency=self.concurrency, + limits=BatchLimits(windows=self.batch_size), + max_attempts=3, + ) as pool, + ): + items, metadata = tee(self._items(task, windows, categorical, pool.capabilities["max_context"])) + with self._record_inference_time(): + forecasts = pool.forecast_inputs( + (item[3] for item in items), + task.horizon, + quantile_levels=levels, + ) + for (w, i, targets, _), result in zip(metadata, forecasts, strict=True): + for c, history in targets.items(): + q = np.asarray(result[c].quantile_values).T + if q.shape != (task.horizon, len(levels)) or not np.isfinite(q).all(): + raise ValueError("Invalid forecast shape or non-finite prediction") + bands[w][c][i] = _project(q, history) + + predictions = [] + for window in bands: + per_target = {} + for c, q in window.items(): + if not np.isfinite(q).all(): + raise ValueError("Missing predictions; refusing to export incomplete results") + values = {"predictions": q[..., levels.index(0.5)]} + values.update({str(level): q[..., levels.index(level)] for level in task.quantile_levels}) + per_target[c] = datasets.Dataset( + pa.table( + { + name: pa.FixedSizeListArray.from_arrays(pa.array(a.reshape(-1)), task.horizon) + for name, a in values.items() + } + ) + ) + predictions.append(datasets.DatasetDict(per_target)) + return predictions diff --git a/models/flashpdm/requirements.txt b/models/flashpdm/requirements.txt new file mode 100644 index 0000000..0d3646e --- /dev/null +++ b/models/flashpdm/requirements.txt @@ -0,0 +1 @@ +flashpdm[batch]==0.1.13