diff --git a/UPDTAE.md b/UPDTAE.md
new file mode 100644
index 0000000..31f292a
--- /dev/null
+++ b/UPDTAE.md
@@ -0,0 +1,8 @@
+We have cloned the fev-bench repo into our own repo. In case we want to get the latest updates from fev-bench, execute the functions below:
+```bash
+cd fev
+git pull origin main
+cd ..
+git add fev
+git commit -m "Update fev submodule"
+```
\ No newline at end of file
diff --git a/benchmarks/fev_bench/results/sap-rpt-1.csv b/benchmarks/fev_bench/results/sap-rpt-1.csv
new file mode 100644
index 0000000..2d25f07
--- /dev/null
+++ b/benchmarks/fev_bench/results/sap-rpt-1.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
+SAP-RPT-1,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_gfc12,0.8421366389158141,,423.9002683162689,110,7c9ee0786e7de9fc,False,0.7.1,0.8421366389158141,0.8421366389158141,0.0664920046796325,0.0664920014500239
+SAP-RPT-1,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_gfc14,0.5467140904290092,,84.73995685577393,20,eb4b54a1ec3114bf,False,0.7.1,0.5467140904290092,0.5467140904290092,0.0268386578581637,0.0268386575774654
+SAP-RPT-1,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_gfc17,0.7233090995700018,,680.372795343399,160,2c63b48ca8f6dd9a,False,0.7.1,0.7233090995700018,0.7233090995700018,0.0477050283400443,0.0477050284052732
+SAP-RPT-1,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,1.118876810170182,,1365.367040157318,4116,c475daf9af96ed16,False,0.7.1,1.118876810170182,1.118876810170182,0.2801493203739041,0.2801493319523299
+SAP-RPT-1,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,1.5834192960044473,,11.394501447677612,35,1774b84125cff789,False,0.7.1,1.5834192960044473,1.5834192960044473,0.0718546947600251,0.0718546945864086
+SAP-RPT-1,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,1.3770248055339214,,390.1334991455078,3942,09041d7a8a29c83f,False,0.7.1,1.3770248055339214,1.3770248055339214,0.196723787582264,0.1967237956029585
+SAP-RPT-1,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,2.2742102260146138,,3.2140352725982666,35,bbb0372c6d5883ab,False,0.7.1,2.2742102260146138,2.2742102260146138,0.0731404164892917,0.0731404166794187
+SAP-RPT-1,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,0.5703347102370115,,569.5202925205231,120,4e80c208c8b54e76,False,0.7.1,0.5703347102370115,0.5703347102370115,0.0356270784463754,0.0356270783141811
+SAP-RPT-1,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,0.5115544299237799,,569.5405824184418,120,7a3623a47fa39853,False,0.7.1,0.5115544299237799,0.5115544299237799,0.0308209282440562,0.030820928916984
+SAP-RPT-1,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,0.4451421715268869,,545.4465999603271,120,a900217b9207dc1a,False,0.7.1,0.4451421715268869,0.4451421715268869,0.0268465426636904,0.0268465428498768
+SAP-RPT-1,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,0.7214275971614181,,83.83045935630798,20,fef9300bbbbc0b06,False,0.7.1,0.7214275971614181,0.7214275971614181,0.1263051511768745,0.1263051499645925
+SAP-RPT-1,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,0.6570291930630476,,83.84110069274902,20,b0b822e47c7608d3,False,0.7.1,0.6570291930630476,0.6570291930630476,0.3154768665374539,0.3154768539853428
+SAP-RPT-1,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,0.4943135357937143,,83.90083575248718,20,8f5f11f4c654e31f,False,0.7.1,0.4943135357937143,0.4943135357937143,0.0727134110700733,0.07271341018732
+SAP-RPT-1,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,1.0669444522362066,,83.82739734649658,20,269b47960e0cd2ad,False,0.7.1,1.0669444522362066,1.0669444522362066,0.0472152590458578,0.047215258488843
+SAP-RPT-1,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,0.5584378124192221,,78.1915557384491,20,7087b94a370e1baa,False,0.7.1,0.5584378124192221,0.5584378124192221,0.0966584654287947,0.0966584651815567
+SAP-RPT-1,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,0.2990157048827684,,3141.3408493995667,11150,29ae5d8fec87a1fd,False,0.7.1,0.2990157048827684,0.2990157048827684,0.0996738331555835,0.0996738318501802
+SAP-RPT-1,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,0.3207699468007305,,660.7015767097473,8920,cfc14443664745b2,False,0.7.1,0.3207699468007305,0.3207699468007305,0.0832832736581097,0.0832832710418868
+SAP-RPT-1,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,0.8917252974013528,,760.1409242153168,6502,cc84996f184fc2f6,False,0.7.1,0.8917252974013528,0.8917252974013528,0.3790277162894107,0.3790277144555386
+SAP-RPT-1,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,0.8309912066179319,,1157.388335466385,10000,e1d9809dc251e19b,False,0.7.1,0.8309912066179319,0.8309912066179319,0.0030811323851314,0.0030811323851314
+SAP-RPT-1,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,0.8545343878586623,,334.525066614151,2936,c55b4fd4c5773129,False,0.7.1,0.8545343878586623,0.8545343878586623,0.0981974937790078,0.0981975044413319
+SAP-RPT-1,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,1.0858999412897214,,20055.723327875137,30490,f11a35ff7f7adf0e,False,0.7.1,1.0858999412897214,1.0858999412897214,0.7874874097236721,0.7874874036240522
+SAP-RPT-1,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,1.1402647425814283,,3911.6503961086273,30490,8d7cc2396f52bcbc,False,0.7.1,1.1402647425814283,1.1402647425814283,0.4352330488413168,0.4352330378042166
+SAP-RPT-1,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,1.1644305252864982,,2233.3839082717896,29364,5d40840afc423bdf,False,0.7.1,1.1644305252864982,1.1644305252864982,0.4344006321463467,0.4344006166719606
+SAP-RPT-1,autogluon/fev_datasets,hierarchical_sales_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,,[],[],[],hierarchical_sales_1D,0.7961062367723486,,660.0803575515747,1180,0ecbb4076fd5ba78,False,0.7.1,0.7961062367723486,0.7961062367723486,0.8319621395291936,0.8319621438713438
+SAP-RPT-1,autogluon/fev_datasets,hierarchical_sales_1W,13,10,-130,13,1,,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,,[],[],[],hierarchical_sales_1W,0.8874876666451424,,110.81857490539552,1180,691bd281f0ad7adc,False,0.7.1,0.8874876666451424,0.8874876666451424,0.5625325482361709,0.5625325370352503
+SAP-RPT-1,autogluon/fev_datasets,favorita_stores_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,sales,,"['holiday', 'onpromotion']",['oil_price'],"['city', 'cluster', 'family', 'state', 'store_nbr', 'type']",favorita_stores_1D,1.22653138258911,,8679.33224773407,15790,7c6ded4c862d08bd,False,0.7.1,1.22653138258911,1.22653138258911,0.1488736065097001,0.1488736038786649
+SAP-RPT-1,autogluon/fev_datasets,favorita_stores_1W,13,10,-130,13,1,,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,,['onpromotion'],['oil_price'],"['city', 'cluster', 'family', 'state', 'store_nbr', 'type']",favorita_stores_1W,2.4495987346926404,,1471.030550479889,15790,1cf7145738b8996c,False,0.7.1,2.4495987346926404,2.4495987346926404,0.1319430562872954,0.1319430590422157
+SAP-RPT-1,autogluon/fev_datasets,favorita_stores_1M,12,2,-24,12,1,,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,sales,,['onpromotion'],['oil_price'],"['city', 'cluster', 'family', 'state', 'store_nbr', 'type']",favorita_stores_1M,2.0154607071704307,,206.079658985138,3158,90adf27f0175f9ae,False,0.7.1,2.0154607071704307,2.0154607071704307,0.1198075966262697,0.119807599079116
+SAP-RPT-1,autogluon/fev_datasets,favorita_transactions_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,transactions,,['holiday'],['oil_price'],"['city', 'cluster', 'state', 'store_nbr', 'type']",favorita_transactions_1D,1.3444656787203468,,273.2902774810791,510,93e47f4edb5bcbe1,False,0.7.1,1.3444656787203468,1.3444656787203468,0.0744859234818631,0.0744859235513344
+SAP-RPT-1,autogluon/fev_datasets,favorita_transactions_1W,13,10,-130,13,1,,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,transactions,,[],['oil_price'],"['city', 'cluster', 'state', 'store_nbr', 'type']",favorita_transactions_1W,1.9867056257441849,,45.16550326347351,510,19b5577b63233e9d,False,0.7.1,1.9867056257441849,1.9867056257441849,0.0714463759240513,0.071446376339716
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+SAP-RPT-1,autogluon/fev_datasets,boomlet_1975,24,20,-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_52', 'target_53', 'target_54', 'target_55', 'target_56', 'target_57', 'target_58', 'target_59', 'target_6', 'target_60', 'target_61', 'target_62', 'target_63', 'target_64', 'target_65', 'target_66', 'target_67', 'target_68', 'target_69', 'target_7', 'target_70', 'target_71', 'target_72', 'target_73', 'target_74', 'target_8', 'target_9']",,[],[],[],boomlet_1975,0.4400065143791637,,5662.752003192902,1500,6e1d9424c5ab2214,False,0.7.1,0.4400065143791637,0.4400065143791637,0.2345331785688524,0.2345331785287106
+SAP-RPT-1,autogluon/fev_datasets,boomlet_2187,24,20,-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_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_2187,1.3536067383034598,,7496.662835121155,2000,c858af6e090b65d2,False,0.7.1,1.3536067383034598,1.3536067383034598,0.4612487250509899,0.4612487247292282
+SAP-RPT-1,autogluon/fev_datasets,boomlet_285,60,20,-1200,60,1,,1440,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_28', 'target_29', 'target_3', 'target_30', 'target_31', 'target_32', 'target_33', 'target_34', 'target_35', 'target_36', 'target_37', 'target_38', 'target_39', 'target_4', 'target_40', 'target_41', 'target_42', 'target_43', 'target_44', 'target_45', 'target_46', 'target_47', 'target_48', 'target_49', 'target_5', 'target_50', 'target_51', 'target_52', 'target_53', 'target_54', 'target_55', 'target_56', 'target_57', 'target_58', 'target_59', 'target_6', 'target_60', 'target_61', 'target_62', 'target_63', 'target_64', 'target_65', 'target_66', 'target_67', 'target_68', 'target_69', 'target_7', 'target_70', 'target_71', 'target_72', 'target_73', 'target_74', 'target_8', 'target_9']",,[],[],[],boomlet_285,0.4887522499509579,,5012.06090426445,1500,51287a29c5959696,False,0.7.1,0.4887522499509579,0.4887522499509579,0.2361605499877073,0.2361605500241261
+SAP-RPT-1,autogluon/fev_datasets,boomlet_619,60,20,-1200,60,1,,1440,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,"['target_0', 'target_1', 'target_10', 'target_11', 'target_12', 'target_13', 'target_14', 'target_15', 'target_16', 'target_17', 'target_18', 'target_19', 'target_2', 'target_20', 'target_21', 'target_22', 'target_23', 'target_24', 'target_25', 'target_26', 'target_27', 'target_28', 'target_29', 'target_3', 'target_30', 'target_31', 'target_32', 'target_33', 'target_34', 'target_35', 'target_36', 'target_37', 'target_38', 'target_39', 'target_4', 'target_40', 'target_41', 'target_42', 'target_43', 'target_44', 'target_45', 'target_46', 'target_47', 'target_48', 'target_49', 'target_5', 'target_50', 'target_51', 'target_6', 'target_7', 'target_8', 'target_9']",,[],[],[],boomlet_619,0.4589632152019691,,3734.450880765915,1040,2d774f07b3096707,False,0.7.1,0.4589632152019691,0.4589632152019691,0.3316163619414299,0.3316163618818891
+SAP-RPT-1,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_772,0.476541619112281,,4996.183359861374,1340,cf7b0f7fe86838d5,False,0.7.1,0.476541619112281,0.476541619112281,0.2199261555647534,0.2199261558060398
+SAP-RPT-1,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_963,0.8984920825928828,,2244.027840614319,560,078a1fcb7a454b72,False,0.7.1,0.8984920825928828,0.8984920825928828,0.4294545046674067,0.4294545052507303
+SAP-RPT-1,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,10.308515136997045,,12.343026161193848,60,764e9294023ee081,False,0.7.1,10.308515136997045,10.308515136997045,0.1742780146714631,0.1742780166818704
+SAP-RPT-1,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,5.696495873500432,,5.200271844863892,60,d257091c861020bf,False,0.7.1,5.696495873500432,5.696495873500432,0.2171346394452743,0.2171346388323594
+SAP-RPT-1,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,10.545587246521231,,213.22159671783447,1020,764e9294023ee081,False,0.7.1,10.545587246521231,10.545587246521231,0.1169043663356008,0.1169043659665892
+SAP-RPT-1,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,6.38023191266649,,92.33834648132324,1020,d257091c861020bf,False,0.7.1,6.38023191266649,6.38023191266649,0.1372612053229603,0.1372612055285968
+SAP-RPT-1,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,3.287582249568893,,61.10391569137573,310,03c47cb66fc4f105,False,0.7.1,3.287582249568893,3.287582249568893,0.0600542620672358,0.0600542630650168
+SAP-RPT-1,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,4.197179122036623,,30.075539112091064,310,91f975812d53dcb0,False,0.7.1,4.197179122036623,4.197179122036623,0.0793666660895327,0.079366666906104
+SAP-RPT-1,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,8.364062360508779,,14.251691341400146,310,941764347d4458f0,False,0.7.1,8.364062360508779,8.364062360508779,0.1968558264998673,0.19685582940912
+SAP-RPT-1,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,0.9884734035485124,,145.72684240341187,1980,f9353d7e5df197ee,False,0.7.1,0.9884734035485124,0.9884734035485124,0.025884024359376267,0.025884024616228306
diff --git a/docs/tutorials/04-adapters.ipynb b/docs/tutorials/04-adapters.ipynb
index d0c10cf..db916e1 100644
--- a/docs/tutorials/04-adapters.ipynb
+++ b/docs/tutorials/04-adapters.ipynb
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/models/sap-rpt-1/model.py b/models/sap-rpt-1/model.py
new file mode 100644
index 0000000..b96c87e
--- /dev/null
+++ b/models/sap-rpt-1/model.py
@@ -0,0 +1,296 @@
+"""SAP RPT-1 model wrapper for fev-bench (v0.8.0 format).
+
+SAP RPT-1 is a tabular foundation model applied to time series forecasting
+via per-item tabular regression with temporal feature engineering.
+"""
+
+import warnings
+
+import datasets
+import numpy as np
+import pandas as pd
+import torch
+from scipy import fft
+from scipy.signal import find_peaks
+
+import fev
+from sap_rpt_oss import SAP_RPT_OSS_Regressor
+
+
+class SapRpt1Model(fev.ForecastingModel):
+    """SAP RPT-1 applied to time series via per-item tabular regression."""
+
+    model_name = "sap-rpt-1"
+
+    # RPT-1 is a general tabular model, not pretrained on any time series datasets
+    trained_on_datasets = []
+
+    def __init__(self, max_context_size: int = 4096, bagging: int = 2):
+        super().__init__()
+        self.max_context_size = max_context_size
+        self.bagging = bagging
+        self._regressor = None
+
+    @property
+    def regressor(self):
+        if self._regressor is None:
+            self._regressor = SAP_RPT_OSS_Regressor(
+                max_context_size=self.max_context_size,
+                bagging=self.bagging,
+            )
+            self._regressor.dtype = torch.float32
+            self._regressor.model = self._regressor.model.float()
+        return self._regressor
+
+    def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:
+        has_static = bool(getattr(task, "static_columns", None))
+        is_multivariate = len(task.target_columns) > 1
+        target_col = "target" if is_multivariate else task.target_columns[0]
+        past_dynamic_cols = list(getattr(task, "past_dynamic_columns", []) or [])
+
+        predictions_per_window = []
+
+        for window in task.iter_windows():
+            train_df, future_df, static_df = fev.convert_input_data(
+                window, adapter="pandas", as_univariate=is_multivariate
+            )
+
+            X_train, y_train, X_future = _prepare_tabular_data(
+                train_df, future_df,
+                static_df=static_df if has_static else None,
+                target_col=target_col,
+                use_static=has_static,
+                past_dynamic_cols=past_dynamic_cols,
+            )
+
+            X_train, X_future = _add_temporal_features(X_train, y_train, X_future)
+
+            # Per-item fit/predict
+            y_pred = np.full(len(X_future), np.nan)
+            item_ids = X_future["id"].unique()
+
+            for sid in item_ids:
+                train_mask = X_train["id"] == sid
+                future_mask = X_future["id"] == sid
+
+                X_tr = X_train[train_mask].reset_index(drop=True)
+                y_tr = y_train[train_mask].reset_index(drop=True)
+                X_fu = X_future[future_mask].reset_index(drop=True)
+
+                if len(X_tr) == 0:
+                    y_pred[future_mask.values] = y_train.mean()
+                    continue
+
+                with self._record_inference_time():
+                    self.regressor.fit(X_tr, y_tr)
+                    preds = self.regressor.predict(X_fu)
+
+                preds = np.where(np.isfinite(preds), preds, y_tr.mean())
+                y_pred[future_mask.values] = preds
+
+            # Fallback for remaining NaNs
+            invalid = ~np.isfinite(y_pred)
+            if invalid.any():
+                y_pred[invalid] = y_train.mean()
+
+            # Format predictions
+            preds_fev = _format_predictions(
+                y_pred, future_df, task.horizon,
+                quantile_levels=task.quantile_levels,
+            )
+
+            if is_multivariate:
+                predictions_per_window.append(
+                    fev.combine_univariate_predictions_to_multivariate(
+                        preds_fev, target_columns=task.target_columns,
+                    )
+                )
+            else:
+                predictions_per_window.append(preds_fev)
+
+        return predictions_per_window
+
+
+# ==============================================================================
+# Data Processing
+# ==============================================================================
+
+
+def _fix_arrow_dtypes(df):
+    df = df.copy()
+    for col in df.columns:
+        dtype_str = str(df[col].dtype)
+        if "timestamp" in dtype_str.lower() or "datetime" in dtype_str.lower():
+            col_dt = pd.to_datetime(df[col])
+            if col_dt.dt.tz is not None:
+                col_dt = col_dt.dt.tz_convert(None)
+            df[col] = col_dt
+        elif "[pyarrow]" in dtype_str or dtype_str == "str":
+            converted = pd.to_numeric(df[col], errors="coerce")
+            df[col] = converted if converted.notna().mean() > 0.5 else df[col].astype(str).astype("object")
+        elif dtype_str == "float32":
+            df[col] = df[col].astype("float64")
+        elif "bool" in dtype_str:
+            df[col] = df[col].astype(int)
+    return df
+
+
+def _prepare_tabular_data(train_df, future_df, static_df=None, target_col="target",
+                          use_static=True, past_dynamic_cols=None):
+    past_dynamic_cols = past_dynamic_cols or []
+
+    if use_static and static_df is not None:
+        train_merged = train_df.merge(static_df, on="id", how="left")
+        future_merged = future_df.merge(static_df, on="id", how="left")
+    else:
+        train_merged = train_df.copy()
+        future_merged = future_df.copy()
+
+    # Drop past-dynamic columns
+    for c in past_dynamic_cols:
+        if c in train_merged.columns:
+            train_merged = train_merged.drop(columns=[c])
+        if c in future_merged.columns:
+            future_merged = future_merged.drop(columns=[c])
+
+    y_train = train_merged[target_col].astype("float64")
+    X_train = train_merged.drop(columns=[target_col])
+
+    valid = ~y_train.isna()
+    X_train = X_train[valid].reset_index(drop=True)
+    y_train = y_train[valid].reset_index(drop=True)
+
+    X_future = future_merged.reindex(columns=X_train.columns)
+
+    X_train = _fix_arrow_dtypes(X_train)
+    X_future = _fix_arrow_dtypes(X_future)
+
+    if "timestamp" in X_train.columns:
+        X_train["timestamp"] = pd.to_datetime(X_train["timestamp"])
+        X_future["timestamp"] = pd.to_datetime(X_future["timestamp"])
+
+    return X_train, y_train, X_future
+
+
+# ==============================================================================
+# Temporal Feature Engineering
+# ==============================================================================
+
+
+def _add_temporal_features(X_train, y_train, X_future, max_seasonal=5):
+    combined = pd.concat([
+        X_train.assign(_is_train=True),
+        X_future.assign(_is_train=False),
+    ], ignore_index=True)
+    combined = combined.sort_values(["id", "timestamp"])
+    combined["running_index"] = combined.groupby("id").cumcount()
+
+    X_train = combined[combined["_is_train"]].drop(columns=["_is_train"]).reset_index(drop=True)
+    X_future = combined[~combined["_is_train"]].drop(columns=["_is_train"]).reset_index(drop=True)
+
+    # Calendar features
+    for df in [X_train, X_future]:
+        if "timestamp" not in df.columns:
+            continue
+    X_train = _add_calendar(X_train)
+    X_future = _add_calendar(X_future)
+
+    # Seasonal features (FFT-based)
+    X_train = X_train.copy()
+    X_train["_target"] = y_train.values
+
+    for i in range(max_seasonal):
+        X_train[f"seasonal_sin_{i}"] = 0.0
+        X_train[f"seasonal_cos_{i}"] = 0.0
+        X_future[f"seasonal_sin_{i}"] = 0.0
+        X_future[f"seasonal_cos_{i}"] = 0.0
+
+    for item_id in X_train["id"].unique():
+        tmask = X_train["id"] == item_id
+        target = X_train.loc[tmask, "_target"].values
+        periods = _detect_periods(target, max_top_k=max_seasonal)
+
+        tidx = X_train.loc[tmask, "running_index"].values
+        for i, p in enumerate(periods[:max_seasonal]):
+            X_train.loc[tmask, f"seasonal_sin_{i}"] = np.sin(2 * np.pi * tidx / p)
+            X_train.loc[tmask, f"seasonal_cos_{i}"] = np.cos(2 * np.pi * tidx / p)
+
+        fmask = X_future["id"] == item_id
+        if fmask.any():
+            fidx = X_future.loc[fmask, "running_index"].values
+            for i, p in enumerate(periods[:max_seasonal]):
+                X_future.loc[fmask, f"seasonal_sin_{i}"] = np.sin(2 * np.pi * fidx / p)
+                X_future.loc[fmask, f"seasonal_cos_{i}"] = np.cos(2 * np.pi * fidx / p)
+
+    X_train = X_train.drop(columns=["_target"])
+    return X_train, X_future
+
+
+def _add_calendar(df):
+    if "timestamp" not in df.columns:
+        return df
+    df = df.copy()
+    ts = pd.to_datetime(df["timestamp"])
+    specs = [
+        ("hour_of_day", ts.dt.hour, 24),
+        ("day_of_week", ts.dt.dayofweek, 7),
+        ("day_of_month", ts.dt.day, 30.5),
+        ("day_of_year", ts.dt.dayofyear, 365),
+        ("week_of_year", ts.dt.isocalendar().week.astype(int), 52),
+        ("month_of_year", ts.dt.month, 12),
+    ]
+    for name, feat, period in specs:
+        df[f"{name}_sin"] = np.sin(2 * np.pi * feat.values / max(period - 1, 1))
+        df[f"{name}_cos"] = np.cos(2 * np.pi * feat.values / max(period - 1, 1))
+    df["year"] = ts.dt.year
+    return df
+
+
+def _detect_periods(values, max_top_k=5, threshold=0.05):
+    values = np.array(values, dtype=float)
+    values = values[~np.isnan(values)]
+    if len(values) < 4:
+        return []
+    n = len(values)
+    # Detrend
+    idx = np.arange(n)
+    coeffs = np.polyfit(idx, values, 1, rcond=None)
+    values = values - np.polyval(coeffs, idx)
+    # Hann + zero-pad + FFT
+    values = values * np.hanning(n)
+    padded = np.zeros(n * 2)
+    padded[:n] = values
+    mag = np.abs(fft.rfft(padded))
+    freqs = np.fft.rfftfreq(n * 2, d=1.0)
+    mag[0] = 0.0
+    peaks, _ = find_peaks(mag, height=threshold * mag.max())
+    if len(peaks) == 0:
+        peaks = np.arange(1, len(mag))
+    top = peaks[np.argsort(mag[peaks])[::-1]][:max_top_k]
+    periods = []
+    for i in top:
+        if freqs[i] > 0:
+            p = round(1.0 / freqs[i])
+            if p > 0 and p not in periods:
+                periods.append(p)
+    return periods[:max_top_k]
+
+
+# ==============================================================================
+# Prediction Formatting
+# ==============================================================================
+
+
+def _format_predictions(y_pred, future_df, horizon, quantile_levels=None):
+    future_df = future_df.copy()
+    future_df["pred"] = y_pred
+    rows = []
+    for sid in sorted(future_df["id"].unique()):
+        preds = future_df[future_df["id"] == sid].sort_values("timestamp")["pred"].tolist()
+        assert len(preds) == horizon
+        row = {"predictions": preds}
+        if quantile_levels:
+            for q in quantile_levels:
+                row[str(q)] = preds
+        rows.append(row)
+    return rows
\ No newline at end of file
diff --git a/models/sap-rpt-1/requirements.txt b/models/sap-rpt-1/requirements.txt
new file mode 100644
index 0000000..6a9c07d
--- /dev/null
+++ b/models/sap-rpt-1/requirements.txt
@@ -0,0 +1,2 @@
+sap-rpt-oss @ git+https://github.com/SAP-samples/sap-rpt-1-oss
+scipy>=1.10.0
\ No newline at end of file