Chapter One: Exploratory Time Series Data Analysis(FREE)
Learn how to organize and visualize time series data in R.
Chapter Two: Predicting the Future
Conduct trend spotting, learn the white noise model, the random walk model, and the definition of stationary processes.
Chapter Three: Correlation Analysis and the Autocorrelation Function
Review the correlation coefficient, then practice estimating and visualizing autocorrelations for time series data.
Chapter Four: Autoregression
Discover the autoregressive model and several of its basic properties.
Chapter Five: A Simple Moving Average
Learn about the simple moving average model, then compare the performance of several models.
Chapter One: Time Series Data and Models
Investigate time series data and learn the basics of ARMA models, which can explain the behavior of such data.
Chapter Two: Fitting ARMA Models
Discover the wonderful world of ARMA models and learn how to fit these models to time series data.
Chapter Three: ARIMA Models
Learn about integrated ARMA (ARIMA) models for nonstationary time series.
Chapter Four: Seasonal ARIMA
Learn how to fit and forecast seasonal time series data using seasonal ARIMA models.
