slug,dataset,dataset_title,horizon,window_id,target_start,target_end,mae,rmse,smape_pct,num_covariates,why_selected,image
stromlast_12h,gefcom_load,GEFCom2014 electricity load,12h,3,2011-11-27 12:00:00,2011-11-27 23:00:00,1.942972183227539,2.40639178957606,1.7117489535380972,34,Sehr niedriger relativer Fehler und klare Tagesprofiltreue.,assets/stromlast_12h.svg
transformer_24h,ett_transformer,Electricity Transformer Temperature,24h,1,2018-06-20 20:00:00,2018-06-21 19:00:00,0.331743876139323,0.3760445589887671,3.693959936094128,12,Stabiler Asset-Zustandsforecast mit Last- und Kalenderkontext.,assets/transformer_24h.svg
pv_1w,gefcom_solar,GEFCom2014 solar generation,1 Woche,3,2014-06-17 01:00:00,2014-06-24 00:00:00,0.041031470355667,0.084806283583168,133.71891891503748,18,Mehrere Tagesprofile in einem langen wettergetriebenen Horizont.,assets/pv_1w.svg
seoul_bike_12h,seoul_bike,Seoul Bike Sharing Demand,12h,5,2018-11-30 00:00:00,2018-11-30 11:00:00,30.68428039550781,40.910082126984136,7.374110539902031,20,Guter wetter- und kalendergetriebener Nachfrageforecast.,assets/seoul_bike_12h.svg
appliances_1w,appliances_energy,Appliances Energy Prediction,1 Woche,3,2016-05-13 19:00:00,2016-05-20 18:00:00,20.48222980045137,36.77532687371412,20.74345960798045,31,Smart-Building-aehnliche Last mit vielen Innen-/Aussenklima-Signalen.,assets/appliances_1w.svg