LEADER 04356nam 22008175 450 001 9910619280803321 005 20240130150100.0 010 $a3-031-13584-9 024 7 $a10.1007/978-3-031-13584-2 035 $a(MiAaPQ)EBC7119383 035 $a(Au-PeEL)EBL7119383 035 $a(CKB)25176461900041 035 $a(DE-He213)978-3-031-13584-2 035 $a(PPN)265856515 035 $a(EXLCZ)9925176461900041 100 $a20221019d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Time Series Analysis and Forecasting with Python /$fby Changquan Huang, Alla Petukhina 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (377 pages) 225 1 $aStatistics and Computing,$x2197-1706 311 08$aPrint version: Huang, Changquan Applied Time Series Analysis and Forecasting with Python Cham : Springer International Publishing AG,c2022 9783031135835 320 $aIncludes bibliographical references and index. 327 $a1. Time Series Concepts and Python -- 2. Exploratory Time Series Data Analysis -- 3. Stationary Time Series Models -- 4. ARMA and ARIMA Modeling and Forecasting -- 5. Nonstationary Time Series Models -- 6. Financial Time Series and Related Models -- 7. Multivariate Time Series Analysis -- 8. State Space Models and Markov Switching Models -- 9. Nonstationarity and Cointegrations -- 10. Modern Machine Learning Methods for Time Series Analysis. 330 $aThis textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems. 410 0$aStatistics and Computing,$x2197-1706 606 $aTime-series analysis 606 $aStatistics$xComputer programs 606 $aEconometrics 606 $aPython (Computer program language) 606 $aMachine learning 606 $aStatistics 606 $aTime Series Analysis 606 $aStatistical Software 606 $aEconometrics 606 $aPython 606 $aMachine Learning 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aAnàlisi de sèries temporals$2thub 606 $aPython (Llenguatge de programació)$2thub 608 $aLlibres electrònics$2thub 615 0$aTime-series analysis. 615 0$aStatistics$xComputer programs. 615 0$aEconometrics. 615 0$aPython (Computer program language). 615 0$aMachine learning. 615 0$aStatistics. 615 14$aTime Series Analysis. 615 24$aStatistical Software. 615 24$aEconometrics. 615 24$aPython. 615 24$aMachine Learning. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 7$aAnàlisi de sèries temporals 615 7$aPython (Llenguatge de programació) 676 $a813 676 $a519.55 700 $aHuang$b Changquan$01262946 702 $aPetukhina$b Alla 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910619280803321 996 $aApplied Time Series Analysis and Forecasting with Python$92954958 997 $aUNINA