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Time series for data science : analysis and forecasting

By: Woodward, Wayne A.
Contributor(s): Sadler, Bivin Philip | Robertson, Stephen.
Series: Chapman & Hall/CRC texts in statistical science series.Publisher: Boca Raton : CRC Press, 2022Description: xxi, 506 p. ; ill., 26 cm.ISBN: 9780367537944.Subject(s): Autoregression | Statistics | Big data | Time-series analysis | Information scienceDDC classification: 519.55 Summary: This 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.
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Item type Current location Call number Status Date due Barcode
Books 519.55 WOO (Browse shelf) Available 034252

Includes bibliographical references and index.

This 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.

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