000			a
999			_c32346 _d32346
008			230903b xxu||||| |||| 00| 0 eng d
020			_a9780367537944
082			_a519.55 _bWOO
100			_aWoodward, Wayne A.
245			_aTime series for data science : analysis and forecasting
260			_bCRC Press, _c2022 _a Boca Raton :
300			_axxi, 506 p. ; _bill., _c26 cm
365			_b99.99 _cGBP _d110.40
490			_aChapman & Hall/CRC texts in statistical science series
504			_aIncludes bibliographical references and index.
520			_aThis book will be written at a level that requires little or no calculus but does not shy away from giving students more than a cursory understanding of the fundamentals and techniques involved. It will cover time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It gives more emphasis to classical ARMA and ARIMA models than is found in similar-level texts. Knowing that students and practitioners want to find a forecast that "works" and don't want to be constrained to a single forecasting strategy, we discuss techniques of ensemble modeling for combining information from several strategies (multivariate, VAR, neural networks, etc.
650			_aAutoregression
650			_aStatistics
650			_aBig data
650			_aTime-series analysis
650			_aInformation science
700			_aSadler, Bivin Philip
700			_aRobertson, Stephen
942			_2ddc _cBK