Description
Product ID: | 9780367537944 |
Product Form: | Hardback |
Country of Manufacture: | GB |
Series: | Chapman & Hall/CRC Texts in Statistical Science |
Title: | Time Series for Data Science |
Subtitle: | Analysis and Forecasting |
Authors: | Author: Bivin Philip Sadler, Wayne A. Woodward, Stephen Robertson |
Page Count: | 528 |
Subjects: | Data science and analysis: general, Data analysis: general, Probability and statistics, Probability & statistics |
Description: | 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. Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting 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. This book 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. Features:
|
Imprint Name: | Chapman & Hall/CRC |
Publisher Name: | Taylor & Francis Ltd |
Country of Publication: | GB |
Publishing Date: | 2022-08-01 |