Time Series Analysis and Forecasting using Python & R

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.