diff --git a/benchmarks/fev_bench/models.yaml b/benchmarks/fev_bench/models.yaml
index d8c0573..506e305 100644
--- a/benchmarks/fev_bench/models.yaml
+++ b/benchmarks/fev_bench/models.yaml
@@ -33,6 +33,14 @@ flowstate:
   zero_shot: true
   commercial_use: true
 
+iamtime:
+  organization: Walmart Labs
+  url: https://huggingface.co/anishsaha/iAmTime-base-forecast
+  model_type: pretrained
+  zero_shot: true
+  commercial_use: true
+  display_name: iAmTime-base-forecast
+
 moirai-2_0:
   organization: Salesforce
   url: https://huggingface.co/Salesforce/moirai-2.0-R-small
diff --git a/benchmarks/fev_bench/results/iamtime.csv b/benchmarks/fev_bench/results/iamtime.csv
new file mode 100644
index 0000000..c506962
--- /dev/null
+++ b/benchmarks/fev_bench/results/iamtime.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
+iAmTime,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,,0.6756500868597637,0.0,1.0469148545525968,110,70e6cce11b73757a,False,0.11.0.dev0,0.6756500868597637,0.8430797870857594,0.07209293145695353,0.057455550060962146,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.3899898428115766,0.0,1.0303257498890162,20,87312edc76952605,False,0.11.0.dev0,0.3899898428115766,0.48340726765295783,0.023637310763255738,0.01908364154269091,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.5080254163742223,0.0,1.677659516222775,160,897258c227dde589,False,0.11.0.dev0,0.5080254163742223,0.6451897076091301,0.04166070147785452,0.03276773451057104,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.8474153166997788,0.0,14.301279596518725,4116,2c9ecdd477948cce,False,0.11.0.dev0,0.8474153166997788,1.0597840759448682,0.2630583470148841,0.20959715883941388,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,1.0162135470767448,0.0,0.289145125541836,35,d4ccf61e0ac3ccdf,False,0.11.0.dev0,1.0162135470767448,1.2547498000374788,0.05935689685901781,0.04814406619825259,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,1.108373118178076,0.0,4.693253712262958,3942,149ec34eb852d454,False,0.11.0.dev0,1.108373118178076,1.370366019907301,0.20214639584122848,0.1614776754832455,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,1.2846542361580533,0.0,0.2664774036966264,35,4eea90b920dec215,False,0.11.0.dev0,1.2846542361580533,1.5774772829437378,0.05152894147438967,0.04173588232418517,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.3802249894013288,0.0,1.816541335079819,120,8facdab2c9ff5bd5,False,0.11.0.dev0,0.3802249894013288,0.4774795180648425,0.03357975366784011,0.026641798114418202,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.4132058029120591,0.0,1.8165569500997663,120,98ab81cde986fe22,False,0.11.0.dev0,0.4132058029120591,0.5161427116480519,0.0323568029292297,0.025946105883417607,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.401051103628092,0.0,1.7860112362541258,120,c1b23331fc6093bc,False,0.11.0.dev0,0.401051103628092,0.48739533978292265,0.029573841987749528,0.024596379537525796,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.4616591817190245,0.0,1.0617191391065717,20,2b9df413e5ec356e,False,0.11.0.dev0,0.4616591817190245,0.5815014884007017,0.10308148587918493,0.08177794379528006,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.43470426610558555,0.0,1.0640261848457158,20,bc6dede7cf6a507b,False,0.11.0.dev0,0.43470426610558555,0.537038322220788,0.2844373947668808,0.2304149812715484,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.3144576383408427,0.0,1.0577149130403996,20,a00b09fd79e0529e,False,0.11.0.dev0,0.3144576383408427,0.3887778570573426,0.0581777035764125,0.04726076236699068,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.6143516066278311,0.0,1.064496855251491,20,9830dda233defc6c,False,0.11.0.dev0,0.6143516066278311,0.8026756124614473,0.03577869182165909,0.02747178500318425,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.364008609624346,0.0,1.0692094913683832,20,9228310bf7192a0a,False,0.11.0.dev0,0.364008609624346,0.4552066548067552,0.07957024236255812,0.06387003523522768,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.8383303438360862,0.0,16.799857958685607,11150,83a79b0a9a999f56,False,0.11.0.dev0,0.8383303438360862,1.0509041786590108,0.35393450586900965,0.28263207039040517,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.27071950524788013,0.0,8.884635468479246,8920,28f46b06b50c6d84,False,0.11.0.dev0,0.27071950524788013,0.3280062512274031,0.08512582113843406,0.07032850965611323,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.6770428664905956,0.0,6.391059829853475,6502,eb8da1769444e614,False,0.11.0.dev0,0.6770428664905956,0.8450141034922565,0.3559830262179817,0.28027905677582177,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.5663987052878219,0.0,9.823140250518918,10000,003815c209114c79,False,0.11.0.dev0,0.5663987052878219,0.72350656898393,0.002659603084901276,0.002078557943616091,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.6261952340148471,0.0,3.107472854666412,2936,5de0711be36aab0b,False,0.11.0.dev0,0.6261952340148471,0.7963555626504034,0.09126590528688827,0.07163229949127703,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.7365569148802792,0.0,109.46280082408339,30490,91e836c1eb428f2d,False,0.11.0.dev0,0.7365569148802792,0.8992814287053499,0.7113683742339189,0.5595326192959601,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.8729750953188183,0.0,34.302983997389674,30490,bb4e7816349be551,False,0.11.0.dev0,0.8729750953188183,1.1052507186196812,0.4115818491890478,0.32358868355025106,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
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+iAmTime,autogluon/fev_datasets,kdd_cup_2022_10T,288,10,-2880,288,1,,144,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,Patv,,[],"['Etmp', 'Itmp', 'Ndir', 'Pab1', 'Pab2', 'Pab3', 'Prtv', 'Wdir', 'Wspd']",[],kdd_cup_2022_10T,,0.4568089334836751,0.0,27.264831634238362,1340,16cef2ee148416f8,False,0.11.0.dev0,0.4568089334836751,0.5482631247643912,1.780427820106059,1.5722216205154316,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,autogluon/fev_datasets,kdd_cup_2022_30T,96,10,-960,96,1,,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,Patv,,[],"['Etmp', 'Itmp', 'Ndir', 'Pab1', 'Pab2', 'Pab3', 'Prtv', 'Wdir', 'Wspd']",[],kdd_cup_2022_30T,,0.44821992884283796,0.0,26.470841604284942,1340,d65c83db5d8286cf,False,0.11.0.dev0,0.44821992884283796,0.5233498299315301,1.6839448747565686,1.6117755729986307,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,autogluon/fev_datasets,kdd_cup_2022_1D,14,10,-140,14,1,,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,Patv,,[],"['Etmp', 'Itmp', 'Ndir', 'Pab1', 'Pab2', 'Pab3', 'Prtv', 'Wdir', 'Wspd']",[],kdd_cup_2022_1D,,0.6980387892037121,0.0,1.8773126336745918,1340,4126635775d467d9,False,0.11.0.dev0,0.6980387892037121,0.8910010707148466,0.7053697142188203,0.5544994743389744,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,autogluon/fev_datasets,uci_air_quality_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,"['C6H6(GT)', 'CO(GT)', 'NO2(GT)', 'NOx(GT)']",,"['AH', 'RH', 'T']",[],[],uci_air_quality_1H,,0.7656393685740268,0.0,1.4242381262592971,80,770ea30aa1f2a0fd,False,0.11.0.dev0,0.7656393685740268,0.9798494823421422,0.31623345425037297,0.24762418768720681,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,autogluon/fev_datasets,uci_air_quality_1D,28,11,-308,28,1,,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,"['C6H6(GT)', 'CO(GT)', 'NO2(GT)', 'NOx(GT)']",,"['AH', 'RH', 'T']",[],[],uci_air_quality_1D,,1.048098708455606,0.0,0.5948593188077211,44,b6e9003deabe1eba,False,0.11.0.dev0,1.048098708455606,1.3110545319890063,0.2719254723679542,0.21606436273571192,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,autogluon/fev_datasets,LOOP_SEATTLE_5T,288,10,-2880,288,1,,288,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,,[],[],[],LOOP_SEATTLE_5T,,0.5181081847341186,0.0,24.03720696317032,3230,7855faba94bb7184,False,0.11.0.dev0,0.5181081847341186,0.6644258606601543,0.07217457133410149,0.05610802472309421,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,autogluon/fev_datasets,LOOP_SEATTLE_1D,28,10,-280,28,1,,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,,[],[],[],LOOP_SEATTLE_1D,,0.7705548442885302,0.0,3.528879832010716,3230,d46589b2c9e45a4d,False,0.11.0.dev0,0.7705548442885302,0.9479744443385727,0.035879354292817904,0.02910204935812105,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
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+iAmTime,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,,1.4404304481157033,0.0,0.6036520190536976,310,dee11a908eddc948,False,0.11.0.dev0,1.4404304481157033,1.7392859038620805,0.02451384639786635,0.02103755358055558,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,1.8011253498783948,0.0,0.5459097758866847,310,05aa7957a46336f0,False,0.11.0.dev0,1.8011253498783948,2.2492619062032384,0.038379311226373294,0.03218357007652957,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,3.6821718832032255,0.0,0.5418058312498033,310,34316f35c64781aa,False,0.11.0.dev0,3.6821718832032255,4.536038724154561,0.06012951927843825,0.04794231765272853,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
+iAmTime,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,,0.5772480701624767,0.0,2.4083807663992047,1980,d404e6be8d6f82fd,False,0.11.0.dev0,0.5772480701624767,0.707637825064422,0.018052004840431447,0.01482263533058687,iamtime,"{""model_path"": ""anishsaha/iAmTime-base-forecast"", ""device"": ""cuda"", ""torch_dtype"": ""bfloat16"", ""batch_size"": 64, ""use_exogenous"": true, ""use_past_dynamic"": true, ""use_known_dynamic"": true}"
diff --git a/models/iamtime/model.py b/models/iamtime/model.py
new file mode 100644
index 0000000..3757aea
--- /dev/null
+++ b/models/iamtime/model.py
@@ -0,0 +1,128 @@
+import datasets
+import numpy as np
+
+import fev
+
+
+class IAmTimeModel(fev.ForecastingModel):
+	"""iAmTime forecasting model from https://github.com/anishsaha12/iAmTime."""
+
+	model_name = "iamtime"
+	trained_on_datasets = []
+
+	def __init__(
+		self,
+		model_path: str = "anishsaha/iAmTime-base-forecast",
+		device: str = "cuda",
+		torch_dtype: str = "bfloat16",
+		batch_size: int = 64,
+		use_exogenous: bool = True,
+		use_past_dynamic: bool = True,
+		use_known_dynamic: bool = True,
+	):
+		super().__init__()
+		if batch_size < 1:
+			raise ValueError("batch_size must be positive")
+		self.model_path = model_path
+		self.device = device
+		self.torch_dtype = torch_dtype
+		self.batch_size = batch_size
+		self.use_exogenous = use_exogenous
+		self.use_past_dynamic = use_past_dynamic
+		self.use_known_dynamic = use_known_dynamic
+		self._pipeline = None
+
+	def _load_pipeline(self):
+		import torch
+		from iAmTime.pipeline import iAmTimePipeline
+
+		if self._pipeline is None:
+			device = self.device
+			if device == "auto":
+				device = "cuda" if torch.cuda.is_available() else "cpu"
+
+			dtype = self.torch_dtype
+			if dtype == "auto":
+				dtype = (
+					torch.bfloat16
+					if device == "cuda" and torch.cuda.is_bf16_supported()
+					else torch.float32
+				)
+			elif isinstance(dtype, str):
+				dtype_names = {"bfloat16": torch.bfloat16, "float32": torch.float32}
+				if dtype not in dtype_names:
+					raise ValueError(f"Unsupported torch_dtype: {dtype}")
+				dtype = dtype_names[dtype]
+
+			self._pipeline = iAmTimePipeline.from_pretrained(
+				self.model_path,
+				device_map=device,
+				torch_dtype=dtype,
+			)
+			self._pipeline.model.eval()
+		return self._pipeline
+
+	def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:
+		import iAmTime.icl_data_factory as IclDataFactory
+
+		pipeline = self._load_pipeline()
+		predictions_per_window = []
+
+		for window in task.iter_windows():
+			past_data, future_data = fev.convert_input_data(
+				window, adapter="datasets", as_univariate=True
+			)
+
+			quantile_levels = task.quantile_levels.copy()
+			if 0.5 not in quantile_levels:
+				quantile_levels.append(0.5)
+
+			quantile_batches = []
+			mean_batches = []
+			for start in range(0, len(past_data), self.batch_size):
+				end = min(start + self.batch_size, len(past_data))
+				batch_past = past_data.select(range(start, end))
+				batch_future = future_data.select(range(start, end))
+				batch_inputs, _, _, _ = IclDataFactory.convert_past_future_datasets_to_icl_input(
+					past_data=batch_past,
+					future_data=batch_future,
+					target_columns=["target"],
+					past_dynamic_columns=window.past_dynamic_columns,
+					known_dynamic_columns=window.known_dynamic_columns,
+					prediction_length=window.horizon,
+					context_length=pipeline.model.iamtime_config.context_length,
+					min_examples=0,
+					max_examples=0,
+					min_past_for_examples=0,
+					use_exogenous=self.use_exogenous,
+					use_past_dynamic=self.use_past_dynamic,
+					use_known_dynamic=self.use_known_dynamic,
+				)
+				with self._record_inference_time():
+					quantiles, mean = pipeline.predict_quantiles(
+						icl_inputs=batch_inputs,
+						prediction_length=window.horizon,
+						quantile_levels=quantile_levels,
+					)
+					quantile_batches.append(quantiles.detach().cpu().numpy())
+					mean_batches.append(mean.detach().cpu().numpy())
+
+			quantiles_np = np.concatenate(quantile_batches, axis=0)
+			mean_np = np.concatenate(mean_batches, axis=0)
+			if task.eval_metric in ["MSE", "RMSE", "RMSSE"]:
+				point_forecast = mean_np
+			else:
+				point_forecast = quantiles_np[:, :, quantile_levels.index(0.5)]
+
+			prediction_columns = {"predictions": point_forecast}
+			for index, level in enumerate(quantile_levels):
+				if level in task.quantile_levels:
+					prediction_columns[str(level)] = quantiles_np[:, :, index]
+
+			predictions = fev.utils.combine_univariate_predictions_to_multivariate(
+				datasets.Dataset.from_dict(prediction_columns),
+				target_columns=window.target_columns,
+			)
+			predictions_per_window.append(predictions)
+
+		return predictions_per_window
diff --git a/models/iamtime/requirements.txt b/models/iamtime/requirements.txt
new file mode 100644
index 0000000..d83cc49
--- /dev/null
+++ b/models/iamtime/requirements.txt
@@ -0,0 +1 @@
+iamtime @ git+https://github.com/anishsaha12/iAmTime.git@2f59d79f476f801f12566dee9466c389adcb8a79