01706ngm 22004573 450 991086730660332120170407160630.0(VaAlASP)3408475(CKB)4940000000254783(OCoLC)986444595(VaAlASP)ASP3408475/marc(EXLCZ)99494000000025478320170407d2012 k| vengur|n||||||||atdirdacontentvrdamediacrdamediavzrdacarriercrrdacarrierCoping mechanisms and defenses. Episode 18, Sublimation 1 /Symptom MediaCarlsbad, Calif. :Symptom Media,2012.1 online resource (2 minutes)Title from resource description page (viewed April 7, 2017).This training title demonstrates Coping Mechanisms and Defenses: Sublimation. Sublimation is seen as the most acceptable of the mechanisms, an expression of anxiety in socially acceptable ways such as arts or sports.Mental illnessDiagnosisCase studiesDefense mechanisms (Psychology)Case studiesAdjustment (Psychology)Case studiesSublimation (Psychology)Case studiesInstructional films.lcgftMental illnessDiagnosisDefense mechanisms (Psychology)Adjustment (Psychology)Sublimation (Psychology)Symptom Media,VaAlASPVaAlASPVIDEO9910867306603321Coping mechanisms and defenses. Episode 18, Sublimation 14170856UNINA04219nam 22005895 450 991088109380332120240815130225.03-031-60339-710.1007/978-3-031-60339-6(MiAaPQ)EBC31605063(Au-PeEL)EBL31605063(CKB)34039650500041(DE-He213)978-3-031-60339-6(EXLCZ)993403965050004120240815d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierStatistical Learning Tools for Electricity Load Forecasting /by Anestis Antoniadis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Jean-Michel Poggi1st ed. 2024.Cham :Springer International Publishing :Imprint: Birkhäuser,2024.1 online resource (232 pages)Statistics for Industry, Technology, and Engineering,2662-55633-031-60338-9 Introduction -- Part I: A Toolbox of Models -- Additive Modelling of Electricity Demand with mgcv -- Probabilistic GAMs: Beyond Mean Modelling -- Functional Time Series -- Random Forests -- Aggregation of Experts -- Mixed Effects Models for Electricity Load Forecasting -- Part II: Case Studies: Models in Action on Specific Applications -- Disaggregated Forecasting of the Total Consumption -- Aggregation of Multi-Scale Experts -- Short-Term Load Forecasting using Fine-Grained Data -- Functional State Space Models -- Forecasting Daily Peak Demand using GAMs -- Forecasting During the Lockdown Period.This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.Statistics for Industry, Technology, and Engineering,2662-5563StatisticsMachine learningStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesStatistical LearningMachine LearningStatistics.Machine learning.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.Statistical Learning.Machine Learning.519Antoniadis Anestis737862Cugliari Jairo1765524Fasiolo Matteo1765525Goude Yannig1765526Poggi Jean-Michel1253641MiAaPQMiAaPQMiAaPQBOOK9910881093803321Statistical Learning Tools for Electricity Load Forecasting4207210UNINA01376nam0 22003131i 450 UON0047922820231205105241.260978-06-911762-8-420170717d2015 |0itac50 baengUS|||| |||||Young Islamthe new politics of religion in Morocco and the Arab WorldAvi Max SpiegelPrinceton, New JerseyPrinceton University Press2015(stampa 2017)X, 246 p.25 cm.001UON000307322001 Princeton Studies in Muslim PoliticsISLAMMaroccoUONC003379FIISLAMPaesi ArabiUONC092267FIUSPrincetonUONL000074297.0964ISLAM - Marocco21SPIEGELAvi MaxUONV236447760577Princeton University PressUONV245813650ITSOL20250530RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00479228SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI C 0302 SI 23586 5 0302 BuonoSIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI2017423 1J 20170717 Young Islam1539173UNIOR