Time Series for Data Science: Analysis and Forecasting
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Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Practical Time Series Analysis for Data Science discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.Practical Time Series Analysis for Data Science is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.
LF/167310/R
Data sheet
- Name of the Author
- & Sadler
Bivin Philip
Bivin Philip & Robertson
Robertson
Sadler
Stephen
Wayne A.
Woodward - Language
- English
- ISBN
- 9780367537944
- Release date
- 2022
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