LEADER 01685nam 2200409 n 450 001 996389613503316 005 20221108091214.0 035 $a(CKB)1000000000646874 035 $a(EEBO)2240885456 035 $a(UnM)99839031 035 $a(EXLCZ)991000000000646874 100 $a19901126d1569 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 00$aMost godly prayers compiled out of Dauids Psalmes by D. Peter Martyr. Translated out of Latine into English by Charles Glemhan. G. Seene and allowed according to the order appointed$b[electronic resource] 210 $aImprinted at London $cBy William Seres$d1569 215 $a[512] p 300 $aA translation, by Charles Glemhan, of the Josias Simmler edition of: Vermigli, Pietro Martire. 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Seene and allowed according to the order appointed$92329006 997 $aUNISA LEADER 04358nam 22008055 450 001 996495169403316 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$b[electronic resource] /$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. 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