This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov
Applied Bayesian Forecasting and Time Series Analysis
โ Scribed by Andy Pole, Mike West, Jeff Harrison (auth.)
- Publisher
- Springer US
- Year
- 1994
- Tongue
- English
- Leaves
- 415
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Content:
Front Matter....Pages i-xix
Front Matter....Pages 1-1
Practical Modelling and Forecasting....Pages 3-11
Methodological Framework....Pages 13-27
Analysis of the DLM....Pages 29-89
Application: Turkey Chick Sales....Pages 91-120
Application: Market Share....Pages 121-146
Application: Marriages in Greece....Pages 147-164
Further Examples and Exercises....Pages 165-231
Front Matter....Pages 233-233
Installing BATS....Pages 235-241
Tutorial: Introduction to BATS....Pages 243-258
Tutorial: Introduction to Modelling....Pages 259-299
Tutorial: Advanced Modelling....Pages 301-340
Tutorial: Modelling with Incomplete Data....Pages 341-357
Tutorial: Data Management....Pages 359-370
Front Matter....Pages 371-371
Communications....Pages 373-380
Menu Descriptions....Pages 381-404
Back Matter....Pages 405-409
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Written for those who need an introduction,<i>Applied Time Series Analysis</i>reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public healt