diff --git a/benchmarks/fev_bench/models.yaml b/benchmarks/fev_bench/models.yaml
index 0b11136..ba9bffa 100644
--- a/benchmarks/fev_bench/models.yaml
+++ b/benchmarks/fev_bench/models.yaml
@@ -158,6 +158,13 @@ lingjiang2_api:
   zero_shot: true
   commercial_use: true
 
+chakrats_api:
+  organization: YHat Labs
+  url: https://huggingface.co/yhatlabs/ChakraTS
+  model_type: closed-api
+  zero_shot: true
+  commercial_use: true
+
 # --- Task-specific models ------------------------------------------------------------------------
 
 deepar:
diff --git a/benchmarks/fev_bench/results/chakrats_api.csv b/benchmarks/fev_bench/results/chakrats_api.csv
new file mode 100644
index 0000000..2f0ae14
--- /dev/null
+++ b/benchmarks/fev_bench/results/chakrats_api.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
+ChakraTS,autogluon/fev_datasets,ETT_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,"['HUFL', 'HULL', 'LUFL', 'LULL', 'MUFL', 'MULL', 'OT']",,[],[],[],ETT_15T,0.5423866249404855,,,280,b25a46033df1ea43,False,0.9.0,0.5423866249404855,0.6884435353918845,0.111708728169081,0.0875571144735756,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,ETT_1D,28,20,-560,28,1,,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,"['HUFL', 'HULL', 'LUFL', 'LULL', 'MUFL', 'MULL', 'OT']",,[],[],[],ETT_1D,1.120323326798492,,,280,09165729d889736c,False,0.9.0,1.120323326798492,1.33429738189409,0.3283153573366119,0.2652983479216268,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,ETT_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,"['HUFL', 'HULL', 'LUFL', 'LULL', 'MUFL', 'MULL', 'OT']",,[],[],[],ETT_1H,0.8520988016108134,,,280,8990bcbad8689c1f,False,0.9.0,0.8520988016108134,1.0885143117095963,0.2377740293191872,0.185838772986483,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,ETT_1W,13,5,-65,13,1,,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,"['HUFL', 'HULL', 'LUFL', 'LULL', 'MUFL', 'MULL', 'OT']",,[],[],[],ETT_1W,2.251779499337926,,,70,7eda996e7e174b76,False,0.9.0,2.251779499337926,2.6080835512830363,0.4923520434533779,0.3894774675048672,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.7511854973865797,,,3230,d46589b2c9e45a4d,False,0.9.0,0.7511854973865797,0.924307477046526,0.0350287309240421,0.0283962612119954,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,LOOP_SEATTLE_1H,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,,[],[],[],LOOP_SEATTLE_1H,0.6352423251154421,,,3230,002ea9451d732fea,False,0.9.0,0.6352423251154421,0.7881578523352093,0.0676273226652072,0.0543957478231581,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.5264660280119604,,,3230,7855faba94bb7184,False,0.9.0,0.5264660280119604,0.666170827182433,0.0721498467622393,0.0569623705147996,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,M_DENSE_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,,[],[],[],M_DENSE_1D,0.6600215557455289,,,300,f18fd4e8b2a34059,False,0.9.0,0.6600215557455289,0.7939796792316123,0.0874672285627278,0.0736490846109151,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,M_DENSE_1H,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,,[],[],[],M_DENSE_1H,0.5676493911261613,,,300,f40bfe9afb7b3e51,False,0.9.0,0.5676493911261613,0.6829186618736695,0.1403808367353634,0.1163021588810774,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,SZ_TAXI_15T,96,10,-960,96,1,,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,,[],[],[],SZ_TAXI_15T,0.3924026064804882,,,1560,4bd3ed28835dc961,False,0.9.0,0.3924026064804882,0.4987904894176981,0.2386330531385025,0.1874438972696233,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,SZ_TAXI_1H,168,2,-336,168,1,,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,,[],[],[],SZ_TAXI_1H,0.396746877277067,,,312,a22b45b2f394c6d4,False,0.9.0,0.396746877277067,0.4811455868641858,0.1612414323232694,0.1337870608846017,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,australian_tourism,8,2,-16,8,1,,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,,[],[],[],australian_tourism,0.6994527601030678,,,178,7013fcb0b68de2bf,False,0.9.0,0.6994527601030678,0.8800364308715163,0.093538178209112,0.0743177282937964,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,bizitobs_l2c_1H,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_0', 'target_1', 'target_2', 'target_3', 'target_4', 'target_5', 'target_6']",,[],[],[],bizitobs_l2c_1H,0.3130003548285518,,,140,b0e17ddbdd539d9e,False,0.9.0,0.3130003548285518,0.3993488721236451,0.3257840609487926,0.2561088472586758,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,bizitobs_l2c_5T,288,20,-5760,288,1,,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_0', 'target_1', 'target_2', 'target_3', 'target_4', 'target_5', 'target_6']",,[],[],[],bizitobs_l2c_5T,0.4223648348295995,,,140,cdf3cd0903314fa4,False,0.9.0,0.4223648348295995,0.5330411374176688,0.532495382548292,0.4313011319076635,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1062,288,20,-5760,288,1,,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_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_3', 'target_4', 'target_5', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1062,0.5481255484556841,,,420,eec81c201b8778e1,False,0.9.0,0.5481255484556841,0.6696937749423006,0.5676759181963243,0.45723403303683,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1209,288,20,-5760,288,1,,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_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_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1209,0.6635766602514787,,,1060,161694bf9dc519eb,False,0.9.0,0.6635766602514787,0.7768357947821151,0.2965807136534005,0.2536099576673025,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1225,60,20,-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_5', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1225,0.1856008810510549,,,980,16ca972263ae30e6,False,0.9.0,0.1856008810510549,0.2314084594981989,0.1217568653437674,0.0979599507471407,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1230,288,20,-5760,288,1,,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_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_3', 'target_4', 'target_5', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1230,1.1542773181223638,,,460,71842e1d5b103a62,False,0.9.0,1.1542773181223638,1.3147501117085931,0.2982646882766625,0.2539871544962799,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1282,60,20,-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_4', 'target_5', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1282,0.4076113044905057,,,700,9387ab4d0c15b634,False,0.9.0,0.4076113044905057,0.4940304486242832,0.3414127263491247,0.278753943917659,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1487,288,20,-5760,288,1,,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_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_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_1487,0.418741875446137,,,1080,acf7534da4aae38f,False,0.9.0,0.418741875446137,0.5066698567349047,0.1994709264872947,0.1646742607359363,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1631,96,20,-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_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_1631,0.5707262361172214,,,800,74d0d6952f666083,False,0.9.0,0.5707262361172214,0.7139540753914254,0.3111350536585973,0.2483567319142898,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1676,96,20,-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_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_75', 'target_76', 'target_77', 'target_78', 'target_79', 'target_8', 'target_80', 'target_81', 'target_82', 'target_83', 'target_84', 'target_85', 'target_86', 'target_87', 'target_88', 'target_89', 'target_9', 'target_90', 'target_91', 'target_92', 'target_93', 'target_94', 'target_95', 'target_96', 'target_97', 'target_98', 'target_99']",,[],[],[],boomlet_1676,0.5564842738170814,,,2000,abbc88acc77da441,False,0.9.0,0.5564842738170814,0.6922892514481791,0.3162984067014592,0.2493611403014009,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,boomlet_1855,24,20,-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_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_1855,0.4445434058597722,,,1040,d0c354b056fd7763,False,0.9.0,0.4445434058597722,0.5237663839846965,0.1558206711176411,0.1327616429492744,chakrats_api,"{""model"": ""chakra-ts-fev""}"
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+ChakraTS,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_gfc17,0.4528877983059727,,,160,897258c227dde589,False,0.9.0,0.4528877983059727,0.5722967361399537,0.0363649042105934,0.0289002900932717,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,redset_15T,96,10,-960,96,1,,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,,[],[],['subset'],redset_15T,0.6839318270923098,,,1260,08bf434cbfb37dc0,False,0.9.0,0.6839318270923098,0.8171297762598015,0.264017963316288,0.2180827801284766,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,redset_1H,24,10,-240,24,1,,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,,[],[],['subset'],redset_1H,1.3054240071021943,,,1380,e8e85a6b0b054d1f,False,0.9.0,1.3054240071021943,1.382891753914324,0.209381413705506,0.1781538581550546,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,redset_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,,[],[],['subset'],redset_5T,0.6306406617490579,,,1180,706c03b5beda8537,False,0.9.0,0.6306406617490579,0.7562405325106509,0.3124436021825932,0.259017915332891,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.6943295869276038,,,6502,eb8da1769444e614,False,0.9.0,0.6943295869276038,0.8652819454097359,0.3662582503636066,0.291002774937201,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.9590832121725908,,,35,d4ccf61e0ac3ccdf,False,0.9.0,0.9590832121725908,1.189423490712634,0.056217185165473,0.0453275065944201,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,1.307893874150742,,10.066288948059082,35,4eea90b920dec215,False,0.9.0,1.307893874150742,1.5796478912497196,0.0515429832199247,0.0425204420439253,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.8641928613912648,,,4116,2c9ecdd477948cce,False,0.9.0,0.8641928613912648,1.0830042769768309,0.2673145835478105,0.2124605009145719,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,1.1423145295813744,,889.0505390167236,3942,149ec34eb852d454,False,0.9.0,1.1423145295813744,1.3977211927291515,0.2107184801363698,0.1699440019078547,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.5327217466120127,,,11150,83a79b0a9a999f56,False,0.9.0,0.5327217466120127,0.5914686609341897,0.1939055547771429,0.1741611636606592,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.2901161545527337,,,8920,28f46b06b50c6d84,False,0.9.0,0.2901161545527337,0.3484746820902829,0.0907668637168558,0.0757623581485784,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,solar_1D,28,10,-280,28,1,,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,,[],[],[],solar_1D,0.5994138158627716,,,1370,ae293fab54bb1beb,False,0.9.0,0.5994138158627716,0.762081464953103,0.2399075106588207,0.1878946017728459,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,solar_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,,[],[],[],solar_1W,1.0941909581476827,,,137,63a28ed11ce85dc9,False,0.9.0,1.0941909581476827,1.467616449651293,0.2478590880260306,0.1848774162579645,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,solar_with_weather_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,,"['day_length', 'humidity', 'pressure', 'rain_1h', 'snow_1h', 'temp', 'wind_speed']","['clouds_all', 'global_horizontal_irradiance']",[],solar_with_weather_15T,0.704858397104833,,,20,6e766232f6e4f0ac,False,0.9.0,0.704858397104833,0.9061647578473536,1.1979196410801196,0.9883732503347666,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,solar_with_weather_1H,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,,"['day_length', 'humidity', 'pressure', 'rain_1h', 'snow_1h', 'temp', 'wind_speed']","['clouds_all', 'global_horizontal_irradiance']",[],solar_with_weather_1H,0.7161476278464917,,,20,12eed0644b253066,False,0.9.0,0.7161476278464917,0.8997136301524675,1.134390568768319,0.9332983662032988,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,1.0796087319230911,,,44,b6e9003deabe1eba,False,0.9.0,1.0796087319230911,1.3601254431937164,0.2764519588090286,0.2178576647189888,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.7956379101833425,,,80,770ea30aa1f2a0fd,False,0.9.0,0.7956379101833425,1.0218989898562358,0.3334073624558995,0.2595354119865108,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,uk_covid_nation_1D,28,20,-560,28,1,,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,"['cumulative_admissions', 'cumulative_cases', 'cumulative_deaths']",,[],"['cumulative_vaccinated_1', 'cumulative_vaccinated_2', 'cumulative_vaccinated_3', 'hospital_cases', 'icu_ventilator_occupancy']",[],uk_covid_nation_1D/cumulative,6.36397839930939,,,240,49ed8b456f954821,False,0.9.0,6.36397839930939,7.445977948648712,0.015773544405751,0.0137666550538225,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,uk_covid_nation_1D,28,20,-560,28,1,,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,"['new_admissions', 'new_cases', 'new_deaths']",,[],"['hospital_cases', 'icu_ventilator_occupancy', 'new_vaccinated_1', 'new_vaccinated_2', 'new_vaccinated_3']",[],uk_covid_nation_1D/new,1.9797412028876464,,,240,49ed8b456f954821,False,0.9.0,1.9797412028876464,2.385644373003469,0.3593151148650255,0.2881605168840193,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,uk_covid_nation_1W,8,4,-32,8,1,,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,"['cumulative_admissions', 'cumulative_cases', 'cumulative_deaths']",,[],"['cumulative_vaccinated_1', 'cumulative_vaccinated_2', 'cumulative_vaccinated_3', 'hospital_cases', 'icu_ventilator_occupancy']",[],uk_covid_nation_1W/cumulative,2.5317196496643097,,,48,92ea0a1b98c9e5ce,False,0.9.0,2.5317196496643097,3.0486104993985843,0.0316398278336887,0.0255522130926851,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,uk_covid_nation_1W,8,4,-32,8,1,,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,"['new_admissions', 'new_cases', 'new_deaths']",,[],"['hospital_cases', 'icu_ventilator_occupancy', 'new_vaccinated_1', 'new_vaccinated_2', 'new_vaccinated_3']",[],uk_covid_nation_1W/new,4.678857719213324,,,48,92ea0a1b98c9e5ce,False,0.9.0,4.678857719213324,5.897480561110073,0.662186173711329,0.5165980927077712,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,uk_covid_utla_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,new_cases,,[],[],[],uk_covid_utla_1D/new,3.7646112842182498,,,2140,da1326db1e917a89,False,0.9.0,3.7646112842182498,4.576466230965323,0.464385004592877,0.3820348176072278,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,uk_covid_utla_1W,13,5,-65,13,1,,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,cumulative_cases,,[],[],[],uk_covid_utla_1W/cumulative,17.34335756316795,,,1070,4c07446a089a723d,False,0.9.0,17.34335756316795,19.231064426867015,0.1890469451442144,0.1695691814902305,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,1.4631318162640992,,,310,dee11a908eddc948,False,0.9.0,1.4631318162640992,1.752100494708957,0.0221486997696985,0.0193515329756846,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,1.7025605450824028,,,310,05aa7957a46336f0,False,0.9.0,1.7025605450824028,2.1277262120234384,0.0316096728774426,0.0267993186325698,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,3.685999857325139,,,310,34316f35c64781aa,False,0.9.0,3.685999857325139,4.450842590081685,0.053725579936187,0.0438888467572642,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,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,0.6896200770033488,,,2936,5de0711be36aab0b,False,0.9.0,0.6896200770033488,0.8638670103057462,0.1001188232915579,0.0800797267068333,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,world_co2_emissions,5,9,-45,5,1,,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,,[],[],[],world_co2_emissions,2.7061759448925575,,,1719,0604d178ae264900,False,0.9.0,2.7061759448925575,3.2556318472738597,0.0854549065459162,0.0703869119742514,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,world_life_expectancy,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,,[],[],[],world_life_expectancy,1.048359947477946,,,2370,34b64743f43fc2a0,False,0.9.0,1.048359947477946,1.241652521148216,0.0099000371770681,0.0084259256399444,chakrats_api,"{""model"": ""chakra-ts-fev""}"
+ChakraTS,autogluon/fev_datasets,world_tourism,5,2,-10,5,1,,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,,[],[],[],world_tourism,3.001530690908532,,,356,893ed5f68fefe071,False,0.9.0,3.001530690908532,3.696652748043766,0.1088651119533275,0.0818991896516085,chakrats_api,"{""model"": ""chakra-ts-fev""}"
diff --git a/models/chakrats_api/model.py b/models/chakrats_api/model.py
new file mode 100644
index 0000000..f6552d8
--- /dev/null
+++ b/models/chakrats_api/model.py
@@ -0,0 +1,121 @@
+"""fev wrapper for ChakraTS, a hosted forecasting model from YHat Labs (https://yhatlabs.com).
+
+ChakraTS is served through an API; no weights are distributed. Set YHAT_API_KEY (evaluation keys are
+issued on request, see https://huggingface.co/yhatlabs/ChakraTS). The wrapper sends every item of a
+window with all of its target columns, past covariates and known covariates, 100 items per call, and
+never mixes evaluation windows in one call. The `chakra-ts-fev` model is the fev-bench evaluation
+configuration of ChakraTS: it declares no overlap with fev-bench datasets.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import os
+import time
+import urllib.error
+import urllib.request
+
+import datasets
+import fev
+import numpy as np
+import pandas as pd
+from fev.model import ForecastingModel
+
+API_URL = os.environ.get("YHAT_API_URL", "https://api.yhatlabs.com/v1/forecast")
+QUANTILES = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+QKEYS = [str(q) for q in QUANTILES]
+MAX_CONTEXT = 20480  # points of history sent per item; longer than every component's own context window
+
+
+def _jsonable(v):
+    """Numbers stay numbers (NaN -> null), text stays text; the API encodes text covariates itself."""
+    if v is None:
+        return None
+    if isinstance(v, bytes):
+        return v.decode()
+    if isinstance(v, str):
+        return v
+    try:
+        f = float(v)
+    except (TypeError, ValueError):
+        return str(v)
+    return None if math.isnan(f) else f
+
+
+def _freq(timestamps) -> str:
+    idx = pd.DatetimeIndex(timestamps[:50])
+    f = pd.infer_freq(idx) if len(idx) >= 3 else None
+    if f is None and len(idx) >= 2:
+        f = pd.tseries.frequencies.to_offset(idx[1] - idx[0]).freqstr
+    return f or "h"
+
+
+class ChakraTSAPIModel(ForecastingModel):
+    model_name = "chakrats_api"
+    trained_on_datasets: list[str] = []
+
+    def __init__(self, model: str = "chakra-ts-fev", batch_size: int = 100, api_key: str | None = None):
+        super().__init__()
+        self.model = model
+        self.batch_size = batch_size
+        self.api_key = api_key or os.environ["YHAT_API_KEY"]
+
+    def _call(self, items: list[dict], horizon: int, freq: str) -> list[dict]:
+        body = {"model": self.model, "horizon": horizon, "freq": freq, "series": items}
+        req = urllib.request.Request(
+            API_URL,
+            data=json.dumps(body).encode(),
+            headers={"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}", "User-Agent": "chakrats-fev-wrapper/1.0 (+https://yhatlabs.com)"},
+        )
+        for attempt in range(8):
+            try:
+                with urllib.request.urlopen(req, timeout=3600) as r:
+                    return json.load(r)["forecasts"]
+            except urllib.error.HTTPError as e:
+                if e.code in (429, 503) and attempt < 7:  # quota window, or the server waking up
+                    time.sleep(min(300, 30 * 2**attempt))
+                    continue
+                raise RuntimeError(f"API error {e.code}: {e.read()[:300]!r}") from None
+        raise RuntimeError("the API kept answering 429/503")
+
+    def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:
+        cols = list(task.target_columns)
+        known = list(task.known_dynamic_columns)
+        past_cols = list(task.past_dynamic_columns)
+        predictions = []
+        for window in task.iter_windows():
+            past, future = window.get_input_data()
+            rows = list(past)
+            fut = {str(r[task.id_column]): r for r in future} if future is not None else {}
+            freq = _freq(rows[0][task.timestamp_column])
+            items = []
+            for r in rows:
+                rid = str(r[task.id_column])
+                item: dict = {"id": rid}
+                if len(cols) == 1:
+                    item["values"] = [_jsonable(v) for v in list(r[cols[0]])[-MAX_CONTEXT:]]
+                else:
+                    item["targets"] = {c: [_jsonable(v) for v in list(r[c])[-MAX_CONTEXT:]] for c in cols}
+                if past_cols:
+                    item["past_covariates"] = {c: [_jsonable(v) for v in list(r[c])[-MAX_CONTEXT:]] for c in past_cols}
+                if known:
+                    item["known_covariates"] = {
+                        c: [_jsonable(v) for v in list(r[c])[-MAX_CONTEXT:]] + [_jsonable(v) for v in fut[rid][c]] for c in known
+                    }
+                items.append(item)
+            out = {}
+            with self._record_inference_time():
+                for i in range(0, len(items), self.batch_size):
+                    for f in self._call(items[i : i + self.batch_size], window.horizon, freq):
+                        out[f["id"]] = f
+            gt_ids = [str(r[task.id_column]) for r in window.get_ground_truth()]
+            q_of = lambda f, c: np.asarray(f["quantiles"] if c is None else f["targets"][c], dtype=np.float32)  # [h, 9]
+            per_col = {}
+            for c in cols:
+                arrs = [q_of(out[g], None if len(cols) == 1 else c) for g in gt_ids]
+                per_col[c] = datasets.Dataset.from_dict(
+                    {"predictions": [a[:, 4].tolist() for a in arrs], **{k: [a[:, j].tolist() for a in arrs] for j, k in enumerate(QKEYS)}}
+                )
+            predictions.append(datasets.DatasetDict(per_col) if len(cols) > 1 else per_col[cols[0]])
+        return predictions
diff --git a/models/chakrats_api/requirements.txt b/models/chakrats_api/requirements.txt
new file mode 100644
index 0000000..73dc132
--- /dev/null
+++ b/models/chakrats_api/requirements.txt
@@ -0,0 +1,3 @@
+datasets==3.6.0
+numpy==2.1.3
+pandas==2.2.3