diff --git a/benchmarks/fev_bench/results/fracast-0.csv b/benchmarks/fev_bench/results/fracast-0.csv
new file mode 100644
index 0000000..6b4698f
--- /dev/null
+++ b/benchmarks/fev_bench/results/fracast-0.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,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
+fracast-0,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_gfc12,0.9029324947360673,0.0,4.3129159990057815,110,70e6cce11b73757a,True,0.11.0.dev0,0.9029324947360673,1.1155546597377406,0.09797330230746154,0.07785153353760363,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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_gfc14,0.8036813291068146,0.0,0.7711854149820283,20,87312edc76952605,True,0.11.0.dev0,0.8036813291068146,1.0116518196212734,0.04976503734711905,0.03951928290267103,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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_gfc17,0.9555249611247204,0.0,3.7701819599606097,160,897258c227dde589,True,0.11.0.dev0,0.9555249611247204,1.2313900962522277,0.08366849197758826,0.06462582546249777,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.2128313510351367,0.0,45.85637249999854,4116,2c9ecdd477948cce,True,0.11.0.dev0,1.2128313510351367,1.4547852728231667,0.39634454671072206,0.3312689605665006,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.1540609473346979,0.0,0.9502661650039954,35,d4ccf61e0ac3ccdf,True,0.11.0.dev0,1.1540609473346979,1.4489090909599756,0.06761316933431466,0.053991890559288956,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.5134354754631667,0.0,61.40153604200168,3942,149ec34eb852d454,True,0.11.0.dev0,1.5134354754631667,1.8635700748852877,0.2928034467162736,0.23888532570179166,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.6390169031579376,0.0,6.383364499997697,35,4eea90b920dec215,True,0.11.0.dev0,1.6390169031579376,2.0639742175500433,0.0677548724159728,0.05402689770818586,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.7045175665325717,0.0,4.603446376946522,120,8facdab2c9ff5bd5,True,0.11.0.dev0,0.7045175665325717,0.8567975114553142,0.06793964840759668,0.055745863069593636,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.7626805373655346,0.0,1.774558667006204,120,98ab81cde986fe22,True,0.11.0.dev0,0.7626805373655346,0.9850750709492546,0.07133201823014959,0.056303062343730184,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.6110721410476141,0.0,4.808016291994136,120,c1b23331fc6093bc,True,0.11.0.dev0,0.6110721410476141,0.7463921892631472,0.049511589797094344,0.04131366064056329,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.6951177234052859,0.0,2.2182279609987745,20,2b9df413e5ec356e,True,0.11.0.dev0,0.6951177234052859,0.8382921086467411,0.15341388879368817,0.12708498150591116,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.1533021598206923,0.0,0.21295558306155726,20,bc6dede7cf6a507b,True,0.11.0.dev0,1.1533021598206923,1.4877428242876374,0.6881687198149408,0.5334686087080712,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.6516971581198601,0.0,0.4314789160271175,20,a00b09fd79e0529e,True,0.11.0.dev0,0.6516971581198601,0.7206385679105194,0.1129809793965894,0.10221619772992792,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.3052752200451736,0.0,0.38458820698724594,20,9830dda233defc6c,True,0.11.0.dev0,1.3052752200451736,1.6443809425158122,0.07433216670833051,0.05868962575158941,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.4904011685289955,0.0,0.37696149898692966,20,9228310bf7192a0a,True,0.11.0.dev0,0.4904011685289955,0.5812545614911402,0.10381130638483278,0.08767936503216074,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.5460126972353514,0.0,84.44377583300229,11150,83a79b0a9a999f56,True,0.11.0.dev0,0.5460126972353514,0.6632889284839563,0.22600883942231365,0.18579444534973305,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.5329999770942466,0.0,121.00068012499833,8920,28f46b06b50c6d84,True,0.11.0.dev0,0.5329999770942466,0.7117557683394238,0.1953733105736431,0.14470945798222212,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.6954294192621363,0.0,83.52040291800222,6502,eb8da1769444e614,True,0.11.0.dev0,0.6954294192621363,0.8761921062288939,0.36880268139161243,0.29051959737566857,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.7905896497067889,0.0,170.12608512501174,10000,003815c209114c79,True,0.11.0.dev0,0.7905896497067889,1.005903745737347,0.003910873061136946,0.0030700804064277337,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.8129118901838216,0.0,75.31952145799005,2936,5de0711be36aab0b,True,0.11.0.dev0,0.8129118901838216,1.014506940898371,0.1259689655166689,0.10181419452765432,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.7418353883508156,0.0,269.8806860830082,30490,91e836c1eb428f2d,True,0.11.0.dev0,0.7418353883508156,0.897676612997825,0.7168476884405084,0.5664283400963503,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.9103450864952889,0.0,273.989163582999,30490,bb4e7816349be551,True,0.11.0.dev0,0.9103450864952889,1.1721196622003305,0.43673959459680767,0.34162609445969594,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,autogluon/fev_datasets,m5_1M,12,1,-12,12,6,,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,,"['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_1M,0.9695038323957577,0.0,236.79168100000243,29364,4dcc997759fda726,True,0.11.0.dev0,0.9695038323957577,1.1539124116810904,0.4207572634075224,0.33530462484165163,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
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+fracast-0,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_772,0.36867661411124963,0.0,9.745736208016751,1340,f6a344659a6a948e,True,0.11.0.dev0,0.36867661411124963,0.39317130844882764,0.18173506546888912,0.1644132660948861,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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_963,0.7541092885827785,0.0,4.84185987697856,560,5bbc9fb121124724,True,0.11.0.dev0,0.7541092885827785,0.8560526910340828,0.4086814180466806,0.34526248277190164,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,4.6655854727655885,0.0,0.7953728800493991,60,74fe4767146a4d96,True,0.11.0.dev0,4.6655854727655885,5.4331334326448495,0.12253307574519079,0.09776842894007108,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,3.0129655555867445,0.0,0.9825602910132147,60,d4ca154cc310644e,True,0.11.0.dev0,3.0129655555867445,3.1712339920201207,0.18805800455012858,0.1536756155197796,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,6.25501403497097,0.0,3.260290959966369,1020,74fe4767146a4d96,True,0.11.0.dev0,6.25501403497097,7.235920484592922,0.0908374418191768,0.07604065686989091,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,4.1147268793783125,0.0,2.931857250005123,1020,d4ca154cc310644e,True,0.11.0.dev0,4.1147268793783125,4.835197542679801,0.1183912531598936,0.09631872544560552,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,1.5198364557019308,0.0,1.2434138740063645,310,dee11a908eddc948,True,0.11.0.dev0,1.5198364557019308,1.9412313556462197,0.027284806689419232,0.02117416044723927,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,2.1415775197773628,0.0,0.9793800409970572,310,05aa7957a46336f0,True,0.11.0.dev0,2.1415775197773628,2.7507768056013946,0.04352275544271726,0.032331412637024125,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,3.648947880168504,0.0,1.0049014580290532,310,34316f35c64781aa,True,0.11.0.dev0,3.648947880168504,5.082264937471707,0.06890376228916044,0.04192554357128122,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
+fracast-0,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,0.6541003027111122,0.0,5.681732541983365,1980,d404e6be8d6f82fd,True,0.11.0.dev0,0.6541003027111122,0.754755428020157,0.01945395478454217,0.01625102910322167,fracast-0,"{""model_id"":""ztxtech/fracast-0"",""weights"":""w8"",""batch_size"":512,""device"":""mps""}"
diff --git a/models/fracast-0/model.py b/models/fracast-0/model.py
new file mode 100644
index 0000000..9ff34b6
--- /dev/null
+++ b/models/fracast-0/model.py
@@ -0,0 +1,203 @@
+"""Fracast-0 adapter for temporary FEV-Bench experiments."""
+from __future__ import annotations
+
+import datasets
+import numpy as np
+import torch
+
+import fev
+
+
+class Fracast0Model(fev.ForecastingModel):
+    model_name = "fracast-0"
+    trained_on_datasets = [
+        "ETT_15T",
+        "ETT_1D",
+        "ETT_1H",
+        "ETT_1W",
+        "LOOP_SEATTLE_1D",
+        "LOOP_SEATTLE_1H",
+        "LOOP_SEATTLE_5T",
+        "M_DENSE_1D",
+        "M_DENSE_1H",
+        "SZ_TAXI_15T",
+        "SZ_TAXI_1H",
+        "australian_tourism",
+        "bizitobs_l2c_1H",
+        "bizitobs_l2c_5T",
+        "boomlet_1062",
+        "boomlet_1209",
+        "boomlet_1225",
+        "boomlet_1230",
+        "boomlet_1282",
+        "boomlet_1487",
+        "boomlet_1631",
+        "boomlet_1676",
+        "boomlet_1855",
+        "boomlet_1975",
+        "boomlet_2187",
+        "boomlet_285",
+        "boomlet_619",
+        "boomlet_772",
+        "boomlet_963",
+        "ecdc_ili",
+        "entsoe_15T",
+        "entsoe_1H",
+        "entsoe_30T",
+        "epf_be",
+        "epf_de",
+        "epf_fr",
+        "epf_np",
+        "epf_pjm",
+        "ercot_1D",
+        "ercot_1H",
+        "ercot_1M",
+        "ercot_1W",
+        "favorita_stores_1D",
+        "favorita_stores_1M",
+        "favorita_stores_1W",
+        "favorita_transactions_1D",
+        "favorita_transactions_1M",
+        "favorita_transactions_1W",
+        "fred_md_2025",
+        "fred_qd_2025",
+        "gvar",
+        "hermes",
+        "hierarchical_sales_1D",
+        "hierarchical_sales_1W",
+        "hospital",
+        "hospital_admissions_1D",
+        "hospital_admissions_1W",
+        "jena_weather_10T",
+        "jena_weather_1D",
+        "jena_weather_1H",
+        "kdd_cup_2022_10T",
+        "kdd_cup_2022_1D",
+        "kdd_cup_2022_30T",
+        "m5_1D",
+        "m5_1M",
+        "m5_1W",
+        "proenfo_gfc12",
+        "proenfo_gfc14",
+        "proenfo_gfc17",
+        "redset_15T",
+        "redset_1H",
+        "redset_5T",
+        "restaurant",
+        "rohlik_orders_1D",
+        "rohlik_orders_1W",
+        "rohlik_sales_1D",
+        "rohlik_sales_1W",
+        "rossmann_1D",
+        "rossmann_1W",
+        "solar_1D",
+        "solar_1W",
+        "solar_with_weather_15T",
+        "solar_with_weather_1H",
+        "uci_air_quality_1D",
+        "uci_air_quality_1H",
+        "uk_covid_nation_1D",
+        "uk_covid_nation_1W",
+        "uk_covid_utla_1D",
+        "uk_covid_utla_1W",
+        "us_consumption_1M",
+        "us_consumption_1Q",
+        "us_consumption_1Y",
+        "walmart",
+        "world_co2_emissions",
+        "world_life_expectancy",
+        "world_tourism",
+    ]
+
+    def __init__(
+        self,
+        model_id: str = "ztxtech/fracast-0",
+        weights: str = "w8",
+        batch_size: int = 512,
+        device: str = "cpu",
+    ):
+        super().__init__()
+        from fracast import FracastModel
+
+        self.model = FracastModel.from_pretrained(
+            model_id, weights=weights, device=device
+        )
+        self.batch_size = int(batch_size)
+
+    @staticmethod
+    def _targets(window: fev.EvaluationWindow) -> list[list[float]]:
+        past_data, _ = fev.convert_input_data(
+            window, adapter="datasets", as_univariate=True
+        )
+        return past_data.with_format("numpy").cast_column(
+            "target", datasets.Sequence(datasets.Value("float32"))
+        )["target"]
+
+    def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:
+        predictions_per_window = []
+        for window in task.iter_windows():
+            targets = self._targets(window)
+            normalized_contexts = []
+            for values in targets:
+                context = np.asarray(values, dtype=np.float32)
+                context = context[-self.model.context_length :]
+                context[~np.isfinite(context)] = np.nan
+                normalized_contexts.append(context)
+            width = max(len(context) for context in normalized_contexts)
+            contexts = np.full(
+                (len(normalized_contexts), width), np.nan, dtype=np.float32
+            )
+            for row_index, context in enumerate(normalized_contexts):
+                contexts[row_index, -len(context) :] = context
+            task_fallback_rows = 0
+            with self._record_inference_time():
+                finite_counts = np.isfinite(contexts).sum(axis=1)
+                fallback_indices = np.flatnonzero(finite_counts < 8)
+                valid_indices = np.flatnonzero(finite_counts >= 8)
+                chunks = [
+                    self.model.forecast_batch(
+                        contexts[valid_indices[start : start + self.batch_size]],
+                        horizon=task.horizon,
+                    )
+                    for start in range(0, len(valid_indices), self.batch_size)
+                ]
+                valid_forecasts = np.concatenate(chunks, axis=0) if chunks else np.empty(
+                    (0, task.horizon, len(task.quantile_levels)), dtype=np.float32
+                )
+                forecasts = np.empty(
+                    (len(contexts), task.horizon, len(task.quantile_levels)),
+                    dtype=np.float32,
+                )
+                forecasts[valid_indices] = valid_forecasts
+                task_fallback_rows = len(fallback_indices)
+                if task_fallback_rows:
+                    for row_index in fallback_indices:
+                        finite_columns = np.flatnonzero(np.isfinite(contexts[row_index]))
+                        fallback_value = (
+                            contexts[row_index, finite_columns[-1]]
+                            if len(finite_columns)
+                            else np.float32(0.0)
+                        )
+                        forecasts[row_index] = fallback_value
+                    print(
+                        f"fracast-0 fallback rows on {task.task_name}: "
+                        f"{task_fallback_rows}",
+                        flush=True,
+                    )
+            predictions_dict = {
+                "predictions": forecasts[:, :, np.argmin(np.abs(np.array(self.model.quantiles) - 0.5))],
+            }
+            if task.quantile_levels != self.model.quantiles:
+                raise ValueError(
+                    f"Fracast-0 quantiles {self.model.quantiles} do not match task "
+                    f"quantiles {task.quantile_levels}"
+                )
+            for level_index, level in enumerate(task.quantile_levels):
+                predictions_dict[str(level)] = forecasts[:, :, level_index]
+            predictions_per_window.append(
+                fev.utils.combine_univariate_predictions_to_multivariate(
+                    datasets.Dataset.from_dict(predictions_dict),
+                    target_columns=task.target_columns,
+                )
+            )
+        return predictions_per_window
diff --git a/models/fracast-0/requirements.txt b/models/fracast-0/requirements.txt
new file mode 100644
index 0000000..64f90cc
--- /dev/null
+++ b/models/fracast-0/requirements.txt
@@ -0,0 +1,2 @@
+fracast==0.1.0
+torch>=2.0