In This Chapter
Understanding the properties of time series
Transforming data to fit modeling assumptions
Forecasting a time series using ARIMA modeling
Seeing how simulation is used for forecasting purposes
You can use a few different techniques to forecast the future values of a time series:
Chapter 16 covers time series regression. This chapter covers ARIMA modeling and simulation techniques. ARIMA models use the past values of a time series to develop a forecasting model, whereas simulation techniques are based on a statistical model of the variable that’s being forecast.
ARIMA (autoregressive integrated moving average) modeling uses the past behavior of a time series to determine its key statistical properties and takes this information to develop a forecasting model.
ARIMA modeling is only valid for a time series that’s both stationary and nonseasonal. A time series is stationary if the basic statistical properties of the time series don’t change ...