Time Series Analysis and Forecasting using Python & R

Editora:

Lulu Press

Código:

491_9781716451133

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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 Wheres 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), Holts and Holt-Winters 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.

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