000			01860nam a22002297a 4500
005			20241108121851.0
008			241108b |||||||| |||| 00| 0 eng d
020			_a9783031135866
082			_a519.55 _bHUA
100			_aHuang, Changquan _918599
245			_aApplied time series analysis and forecasting with python
260			_bSpringer _aSwitzerland _c2022
300			_ax, 372 p.
365			_aEURO _b25.20
490			_aStatistics and Computing
520			_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 equallyappeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems. (https://link.springer.com/book/10.1007/978-3-031-13584-2)
650			_aMathematics
650			_aTime-series analysis
650			_aPython (Computer program) _918600
700			_aPetukhina, Alla _918601
942			_cBK _2ddc
999			_c7547 _d7547