Time Series Analysis and Forecasting using Python & R

von Jeffrey Strickland

448 Seiten, Hardcover

Kurzbeschreibung des Verlags

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-19Chapter 4: Simple exponential smoothing (SES), Holt's and Holt-Winter's double and triple exponential smoothingChapter 5: Python programming in Jupyter Notebook for the concepts covered in Chapters 2, 3 and 4Chapter 6: Stationarity and differencing, including unit root tests. Chapter 7: ARIMA and SARMIA (seasonal) modeling and forecast developmentChapter 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.

• Reihe: Lulu.com
• ISBN: 9781716451133
• Sprache: Englisch
• Erscheinungsdatum: 28.11.2020
• Größe: 235 x 157 mm
• Verlag: Lulu Press