LEADER 02851nam 2200709I 450 001 9910789651003321 005 20230814231859.0 010 $a0-429-91087-8 010 $a0-429-47187-4 010 $a0-429-89664-6 010 $a1-283-24890-5 010 $a9786613248909 010 $a1-84940-421-6 035 $a(CKB)2670000000113503 035 $a(EBL)764942 035 $a(OCoLC)748242018 035 $a(SSID)ssj0000534042 035 $a(PQKBManifestationID)12199984 035 $a(PQKBTitleCode)TC0000534042 035 $a(PQKBWorkID)10509980 035 $a(PQKB)11345013 035 $a(MiAaPQ)EBC764942 035 $a(Au-PeEL)EBL764942 035 $a(CaPaEBR)ebr10495828 035 $a(CaONFJC)MIL324890 035 $a(OCoLC)753966142 035 $a(FlBoTFG)9780429471872 035 $a(OCoLC)56593768 035 $a(FINmELB)ELB141707 035 $a(EXLCZ)992670000000113503 100 $a20190122h20182004 uy 0 101 0 $aeng 135 $aur||| ||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAnxiety at 35,000 Feet $eAn Introduction to Clinical Aerospace Psychology /$fby Robert Bor 205 $aFirst edition. 210 1$aBoca Raton, FL :$cRoutledge,$d[2018]. 210 4$d©2004. 215 $a1 online resource (129 p.) 225 1 $aForensic psychotherapy monograph series 300 $aDescription based upon print version of record. 311 $a0-367-32332-X 311 $a1-85575-965-9 320 $aIncludes bibliographical references (p. 95-102) and index. 327 $aCOVER; SERIES FOREWORD; ABOUT THE AUTHORS; FOREWORD; 1 Towards the development ofclinical aerospace psychology; 2 Understanding passenger behaviour; 3 The mental health of pilots; 4 The psychodynamics of travel phobia:a contribution to clinical aerospace psychology; 5 Clinical aerospace psychology in the future:a dialogue; REFERENCES; INDEX 330 3 $aFear of flying is a growing problem among both passengers and airline crews. Recent terrorist attacks have heightened the levels of anxiety and fear when boarding a plane. In this volume, one of Britain's leading aviation psychologists explores passenger behaviour when faced with anxiety towards flying, the mental health of pilots and the possible treatments for people suffering from fear of flying. Includes contributions from Brett Kahr. 410 0$aForensic psychotherapy monograph series. 517 3 $aAnxiety at thirty-five thousand feet 606 $aAviation psychology 606 $aFear of flying 615 0$aAviation psychology. 615 0$aFear of flying. 676 $a155.965 700 $aBor$b Robert$0312844 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910789651003321 996 $aAnxiety at 35,000 Feet$93739071 997 $aUNINA LEADER 04414nam 22006495 450 001 9910881093803321 005 20260113111906.0 010 $a3-031-60339-7 024 7 $a10.1007/978-3-031-60339-6 035 $a(MiAaPQ)EBC31605063 035 $a(Au-PeEL)EBL31605063 035 $a(CKB)34039650500041 035 $a(DE-He213)978-3-031-60339-6 035 $a(EXLCZ)9934039650500041 100 $a20240815d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Learning Tools for Electricity Load Forecasting /$fby Anestis Antoniadis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Jean-Michel Poggi 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2024. 215 $a1 online resource (232 pages) 225 1 $aStatistics for Industry, Technology, and Engineering,$x2662-5563 311 08$a3-031-60338-9 327 $aIntroduction -- 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. 330 $aThis monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives ? generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience. 410 0$aStatistics for Industry, Technology, and Engineering,$x2662-5563 606 $aStatistics 606 $aMachine learning 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aStatistical Learning 606 $aMachine Learning 606 $aEnergia elèctrica$2thub 606 $aModels matemàtics$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aMachine learning. 615 14$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aStatistical Learning. 615 24$aMachine Learning. 615 7$aEnergia elèctrica 615 7$aModels matemàtics 676 $a519 700 $aAntoniadis$b Anestis$0737862 701 $aCugliari$b Jairo$01765524 701 $aFasiolo$b Matteo$01765525 701 $aGoude$b Yannig$01765526 701 $aPoggi$b Jean-Michel$01253641 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910881093803321 996 $aStatistical Learning Tools for Electricity Load Forecasting$94207210 997 $aUNINA