LEADER 01355nam 2200385 a 450 001 9910698728503321 005 20090422112540.0 035 $a(CKB)5470000002396573 035 $a(OCoLC)318986736 035 $a(EXLCZ)995470000002396573 100 $a20090422d2009 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTax gap$b[electronic resource] $eIRS could do more to promote compliance by third parties with miscellaneous income reporting requirements : report to the Committee on Finance, U.S. Senate 210 1$a[Washington, D.C.] :$cU.S. Govt. Accountability Office,$d[2009] 215 $aiii, 52 pages $cdigital, PDF file 300 $aTitle from title screen (viewed on Apr. 14, 2009). 300 $a"January 2009." 300 $a"GAO-09-238." 320 $aIncludes bibliographical references. 517 $aTax gap 606 $aTaxpayer compliance$zUnited States 606 $aTax administration and procedure$zUnited States 615 0$aTaxpayer compliance 615 0$aTax administration and procedure 712 02$aUnited States.$bCongress.$bSenate.$bCommittee on Finance. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910698728503321 996 $aTax gap$93441159 997 $aUNINA LEADER 01149nam0 22002893i 450 001 UFE0695303 005 20231121125902.0 010 $a3484295236 100 $a20160329d2005 ||||0itac50 ba 101 | $ager 102 $ade 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aDeutsche Texte des Mittelalters zwischen Handschriftennähe und Rekonstruktion$eBerliner Fachtagung 1.-3. April 2004$fherausgegeben von Martin J. Schubert 210 $aTübingen$cNiemeyer$d2005 215 $aVI, 330 p.$d24 cm. 225 | $aBeihefte zu Editio$v23 410 0$1001BVE0019585$12001 $aBeihefte zu Editio$v23 702 1$aSchubert$b, Martin J.$3PUVV160754 801 3$aIT$bIT-01$c20160329 850 $aIT-FR0017 899 $aBiblioteca umanistica Giorgio Aprea$bFR0017 912 $aUFE0695303 950 0$aBiblioteca umanistica Giorgio Aprea$d 52CIS 11/72$e 52VM 0000677275 VM barcode:00044495. - Inventario:4788 LLCSVM$fA $h20070322$i20121204 977 $a 52 996 $aDeutsche Texte des Mittelalters zwischen Handschriftennahe und Rekonstruktion$91025696 997 $aUNICAS LEADER 03402oam 2200481 450 001 9910822817603321 005 20210528112543.0 010 $a1-119-68238-X 010 $a1-119-68239-8 010 $a1-119-68237-1 035 $a(CKB)4100000011632866 035 $a(MiAaPQ)EBC6420045 035 $a(OCoLC-P)1226581333 035 $a(PPN)272709751 035 $a(CaSebORM)9781119682363 035 $a(OCoLC)1226581333 035 $a(EXLCZ)994100000011632866 100 $a20210528d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning for time series forecasting with Python /$fFrancesca Lazzeri 210 1$aIndianapolis, Indiana :$cWiley,$d[2021] 210 4$d©2021 215 $a1 online resource (227 pages) 311 $a1-119-68236-3 327 $aOverview of Time Series Forecasting -- How to Design an End-to-End Time Series Forecasting Solution on the Cloud -- Time Series Data Preparation -- Introduction to Autoregressive and Automated Methods for Time Series Forecasting -- Introduction to Neural Networks for Time Series Forecasting -- Model Deployment for Time Series Forecasting. 330 $aLearn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling. 606 $aMachine learning 606 $aPython (Computer program language) 615 0$aMachine learning. 615 0$aPython (Computer program language) 676 $a006.31 700 $aLazzeri$b Francesca$0617825 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910822817603321 996 $aMachine learning for time series forecasting with Python$94055511 997 $aUNINA