Practical time series analysis for data science
"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 …
Read More ↓Title Statements
- Full title :Practical time series analysis for data science / Wayne A. Woodward, Bivin Philip Sadler and Stephen Robertson.
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.)"--
Read More ↓Edition Statements
- Publication Date:2022.
- Edition:First edition
Publication Statements
- Publisher(s):CRC Press
- Place of Publication:Boca Raton
- Language:eng
- Publication Date:2022.
The Physical Object
- Description:1 online resource.
Edition Identifiers
- ISBN:9781003089070, 1003089070, 9781000555363, 1000555364, 9781000555332, 100055533X
- DOI:10.1201/9781003089070
- oclc:on1344765455
- LC Classification:QA280
- Dewey Decimal:519.5/5
Table of Contents
Working with data collected over time -- Exploring time series data -- Statistical basics for time series analysis -- The frequency domains -- ARMA models -- ARMA fitting and forecasting -- ARIMA, seasonal, and ARCH/GARCH models -- Time series regression -- Model assessment -- Multivariate time series -- Deep neural network based time series models.
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