000			01338nam a22002057a 4500
008			240312b2022 |||||||| |||| 00| 0 eng d
020			_a9780367537944
082			_a519.55 WOO-W
100			_aWoodward, Wayne A.
245			_aTime series for data science : _banalysis and forecasting / _cWayne A. Woodward, Bivin P. Sadler and Stephen D. Robertson
260			_aBoca Raton _bCRC Press _c2022
300			_a506p.
440			_aTexts in Statistical Science
500			_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			_a Time-series analysis.
650			_a Autoregression (Statistics)
650			_aBig data.
700			_aSadler, Bivin P.
700			_aRobertson, Stephen D.
999			_c92107 _d92107