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Book synopsis

This book full-color textbook assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, but it is not required. We use current real-world data, like COVID-19, to motivate times series analysis have three thread problems that appear in nearly every chapter: "Got Milk?", "Got a Job?" and "Where's the Beef?"

Chapter 1: Loading data in the R-Studio and Jupyter Notebook environments.

Chapter 2: Components of a times series and decomposition

Chapter 3: Moving averages (MAs) and COVID-19

Chapter 4: Simple exponential smoothing (SES), Holt's and Holt-Winter's double and triple exponential smoothing

Chapter 5: Python programming in Jupyter Notebook for the concepts covered in Chapters 2, 3 and 4

Chapter 6: Stationarity and differencing, including unit root tests.

Chapter 7: ARIMA and SARMIA (seasonal) modeling and forecast development

Chapter 8: ARIMA modeling using Python

Chapter 9: Structural models and analysis using unobserved component models (UCMs)

Chapter 10: Advanced time series analysis, including time-series interventions, exogenous regressors, and vector autoregressive (VAR) processes.

Book details

Book category Books in German Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Programmiersprachen

• Full title: Time Series Analysis and Forecasting using Python & R
• Author: Jeffrey Strickland
• Language: English
• Binding: Hardback
• Number of pages: 448
• EAN: 9781716451133
• ISBN: 1716451132
• ID: 33555638
• Publisher: Lulu.com
• Weight: 767 g
• Dimensions: 229 × 152 × 25 mm
• Date of publishing: 28. November 2020

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