LEADER 03386nam 2200853z- 450 001 9910557390403321 005 20231214133507.0 035 $a(CKB)5400000000041994 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76666 035 $a(EXLCZ)995400000000041994 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFeature Papers of Forecasting 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (186 p.) 311 $a3-0365-1030-3 311 $a3-0365-1031-1 330 $aNowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented. 606 $aResearch & information: general$2bicssc 610 $aDirect Normal Irradiance (DNI) 610 $aIFS/ECMWF 610 $aforecast 610 $aevaluation 610 $aDNI attenuation Index (DAI) 610 $abias correction 610 $anowcast 610 $ameteorological radar data 610 $aoptical flow 610 $adeep learning 610 $aBates-Granger weights 610 $auniform weights 610 $a(REG) ARIMA 610 $aETS 610 $aHodrick-Prescott trend 610 $aGoogle Trends indices 610 $aHimalayan region 610 $astreamflow forecast verification 610 $apersistence 610 $asnow-fed rivers 610 $aintermittent rivers 610 $acostumer relation management 610 $abusiness to business sales prediction 610 $amachine learning 610 $apredictive modeling 610 $amicrosoft azure machine-learning service 610 $atravel time forecasting 610 $atime series 610 $abus service 610 $atransit systems 610 $asustainable urban mobility plan 610 $abus travel time 610 $alearning curve 610 $aforecasting 610 $aproduction cost 610 $acost estimating 610 $asemi-empirical model 610 $alogistic map 610 $aCOVID-19 610 $aSARS-CoV-2 610 $aPV output power estimation 610 $aPV-load decoupling 610 $abehind-the-meter PV 610 $abaseline prediction 615 7$aResearch & information: general 700 $aLeva$b Sonia$4edt$043963 702 $aLeva$b Sonia$4oth 906 $aBOOK 912 $a9910557390403321 996 $aFeature Papers of Forecasting$93023582 997 $aUNINA