Database Reference
In-Depth Information
Summary
This chapter presented time series analysis using ARIMA models. Time series
analysis is different from other statistical techniques in the sense that most
statistical analyses assume the observations are independent of each other. Time
series analysis implicitly addresses the case in which any particular observation is
somewhat dependent on prior observations.
Using differencing, ARIMA models allow nonstationary series to be transformed
into stationary series to which seasonal and nonseasonal ARMA models can be
applied. The importance of using the ACF and PACF plots to evaluate the
autocorrelations was illustrated in determining ARIMA models to consider fitting.
Akaike and Bayesian Information Criteria can be used to compare one fitted ARIMA
model against another. Once an appropriate model has been determined, future
values in the time series can be forecasted.
