Time Series Analysis and Forecasting using Python & R

Time Series Analysis and Forecasting using Python & RThis 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

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.